{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "72cb4fbdcf8a9b882a5dfaa1449a3520ae529639"
   },
   "source": [
    "# House Prices: Advanced Regression Techniques\n",
    "### Predict sales prices and practice feature engineering, RFs, and gradient boosting\n",
    "![main](http://www.propertyreporter.co.uk/images/660x350/16402-shutterstock_538341163.jpg)\n",
    "\n",
    "<br>\n",
    "### Weekly update\n",
    "\n",
    "**Competition Description**\n",
    "\n",
    "Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.\n",
    "\n",
    "With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.\n",
    "\n",
    "**Executive Summary**\n",
    "\n",
    "I started this competition by just focusing on getting a good understanding of the dataset. The EDA is detailed and many visualizations are included. This version also includes modeling:\n",
    "\n",
    "- Lasso regression model (great perform)\n",
    "- XGBoost model\n",
    "- LGBM model\n",
    "- Dragon model\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "### Load packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_kg_hide-input": true,
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['my-best-house-price', 'top-10-0-10943-stacking-mice-and-brutal-force', 'hybrid-svm-benchmark-approach-0-11180-lb-top-2', 'lasso-model-for-regression-problem', 'house-prices-advanced-regression-techniques']\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load in \n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
    "\n",
    "from datetime import datetime\n",
    "from scipy.stats import skew  # for some statistics\n",
    "from scipy.special import boxcox1p\n",
    "from scipy.stats import boxcox_normmax\n",
    "from sklearn.linear_model import ElasticNetCV, LassoCV, RidgeCV\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from mlxtend.regressor import StackingCVRegressor\n",
    "from xgboost import XGBRegressor\n",
    "from lightgbm import LGBMRegressor\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import sklearn.linear_model as linear_model\n",
    "import seaborn as sns\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "import os\n",
    "print(os.listdir(\"../input\"))\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Any results you write to the current directory are saved as output."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_kg_hide-input": true,
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data is loaded!\n"
     ]
    }
   ],
   "source": [
    "train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')\n",
    "test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')\n",
    "print (\"Data is loaded!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_kg_hide-input": true,
    "_uuid": "74dbd74c5d18163b3e9c58a91c008e775415cd1f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train:  1460 sales, and  81 features\n",
      "Test:  1459 sales, and  80 features\n"
     ]
    }
   ],
   "source": [
    "print (\"Train: \",train.shape[0],\"sales, and \",train.shape[1],\"features\")\n",
    "print (\"Test: \",test.shape[0],\"sales, and \",test.shape[1],\"features\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_uuid": "5657e5c58c3f4cb8c0b9012d275fb15f44cee2ef"
   },
   "outputs": [
    {
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       "      <th>Functional</th>\n",
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       "      <th>GarageQual</th>\n",
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       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
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       "      <th>SaleType</th>\n",
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       "      <td>TA</td>\n",
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       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Detchd</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>3</td>\n",
       "      <td>642</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NoRidge</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>350.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Av</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>655</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>490</td>\n",
       "      <td>1145</td>\n",
       "      <td>GasA</td>\n",
       "      <td>...</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1145</td>\n",
       "      <td>1053</td>\n",
       "      <td>0</td>\n",
       "      <td>2198</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>9</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>3</td>\n",
       "      <td>836</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning    ...     SaleType  SaleCondition SalePrice\n",
       "0   1          60       RL    ...           WD         Normal    208500\n",
       "1   2          20       RL    ...           WD         Normal    181500\n",
       "2   3          60       RL    ...           WD         Normal    223500\n",
       "3   4          70       RL    ...           WD        Abnorml    140000\n",
       "4   5          60       RL    ...           WD         Normal    250000\n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_uuid": "5f589f6bd8a1e1773b5ac8251ab15ebb4215d436"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>Condition2</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>Foundation</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>Heating</th>\n",
       "      <th>HeatingQC</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>Feedr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1961</td>\n",
       "      <td>1961</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>Rec</td>\n",
       "      <td>468.0</td>\n",
       "      <td>LwQ</td>\n",
       "      <td>144.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>882.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>896</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>896</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>5</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1961.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>1.0</td>\n",
       "      <td>730.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1958</td>\n",
       "      <td>1958</td>\n",
       "      <td>Hip</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>108.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>923.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>406.0</td>\n",
       "      <td>1329.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1329</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1329</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1958.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>1.0</td>\n",
       "      <td>312.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>393</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gar2</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Gilbert</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1997</td>\n",
       "      <td>1998</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>791.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>928.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>928</td>\n",
       "      <td>701</td>\n",
       "      <td>0</td>\n",
       "      <td>1629</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>Fin</td>\n",
       "      <td>2.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>212</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9978</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Gilbert</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1998</td>\n",
       "      <td>1998</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>20.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>602.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>926.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>926</td>\n",
       "      <td>678</td>\n",
       "      <td>0</td>\n",
       "      <td>1604</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>Fin</td>\n",
       "      <td>2.0</td>\n",
       "      <td>470.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>360</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>120</td>\n",
       "      <td>RL</td>\n",
       "      <td>43.0</td>\n",
       "      <td>5005</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>HLS</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>StoneBr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>TwnhsE</td>\n",
       "      <td>1Story</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1992</td>\n",
       "      <td>1992</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>263.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1280</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1280</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>5</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  MSSubClass MSZoning      ...       YrSold  SaleType SaleCondition\n",
       "0  1461          20       RH      ...         2010        WD        Normal\n",
       "1  1462          20       RL      ...         2010        WD        Normal\n",
       "2  1463          60       RL      ...         2010        WD        Normal\n",
       "3  1464          60       RL      ...         2010        WD        Normal\n",
       "4  1465         120       RL      ...         2010        WD        Normal\n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_kg_hide-input": false
   },
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 1460 instances of training data and 1460 of test data. Total number of attributes equals 81, of which 36 is quantitative, 43 categorical + Id and SalePrice.\n",
    "\n",
    "**Quantitative:** 1stFlrSF, 2ndFlrSF, 3SsnPorch, BedroomAbvGr, BsmtFinSF1, BsmtFinSF2, BsmtFullBath, BsmtHalfBath, BsmtUnfSF, EnclosedPorch, Fireplaces, FullBath, GarageArea, GarageCars, GarageYrBlt, GrLivArea, HalfBath, KitchenAbvGr, LotArea, LotFrontage, LowQualFinSF, MSSubClass, MasVnrArea, MiscVal, MoSold, OpenPorchSF, OverallCond, OverallQual, PoolArea, ScreenPorch, TotRmsAbvGrd, TotalBsmtSF, WoodDeckSF, YearBuilt, YearRemodAdd, YrSold\n",
    "\n",
    "**Qualitative:** Alley, BldgType, BsmtCond, BsmtExposure, BsmtFinType1, BsmtFinType2, BsmtQual, CentralAir, Condition1, Condition2, Electrical, ExterCond, ExterQual, Exterior1st, Exterior2nd, Fence, FireplaceQu, Foundation, Functional, GarageCond, GarageFinish, GarageQual, GarageType, Heating, HeatingQC, HouseStyle, KitchenQual, LandContour, LandSlope, LotConfig, LotShape, MSZoning, MasVnrType, MiscFeature, Neighborhood, PavedDrive, PoolQC, RoofMatl, RoofStyle, SaleCondition, SaleType, Street, Utilities,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [],
   "source": [
    "quantitative = [f for f in train.columns if train.dtypes[f] != 'object']\n",
    "quantitative.remove('SalePrice')\n",
    "quantitative.remove('Id')\n",
    "qualitative = [f for f in train.columns if train.dtypes[f] == 'object']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_kg_hide-input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MSSubClass',\n",
       " 'LotFrontage',\n",
       " 'LotArea',\n",
       " 'OverallQual',\n",
       " 'OverallCond',\n",
       " 'YearBuilt',\n",
       " 'YearRemodAdd',\n",
       " 'MasVnrArea',\n",
       " 'BsmtFinSF1',\n",
       " 'BsmtFinSF2',\n",
       " 'BsmtUnfSF',\n",
       " 'TotalBsmtSF',\n",
       " '1stFlrSF',\n",
       " '2ndFlrSF',\n",
       " 'LowQualFinSF',\n",
       " 'GrLivArea',\n",
       " 'BsmtFullBath',\n",
       " 'BsmtHalfBath',\n",
       " 'FullBath',\n",
       " 'HalfBath',\n",
       " 'BedroomAbvGr',\n",
       " 'KitchenAbvGr',\n",
       " 'TotRmsAbvGrd',\n",
       " 'Fireplaces',\n",
       " 'GarageYrBlt',\n",
       " 'GarageCars',\n",
       " 'GarageArea',\n",
       " 'WoodDeckSF',\n",
       " 'OpenPorchSF',\n",
       " 'EnclosedPorch',\n",
       " '3SsnPorch',\n",
       " 'ScreenPorch',\n",
       " 'PoolArea',\n",
       " 'MiscVal',\n",
       " 'MoSold',\n",
       " 'YrSold']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quantitative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_kg_hide-input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MSZoning',\n",
       " 'Street',\n",
       " 'Alley',\n",
       " 'LotShape',\n",
       " 'LandContour',\n",
       " 'Utilities',\n",
       " 'LotConfig',\n",
       " 'LandSlope',\n",
       " 'Neighborhood',\n",
       " 'Condition1',\n",
       " 'Condition2',\n",
       " 'BldgType',\n",
       " 'HouseStyle',\n",
       " 'RoofStyle',\n",
       " 'RoofMatl',\n",
       " 'Exterior1st',\n",
       " 'Exterior2nd',\n",
       " 'MasVnrType',\n",
       " 'ExterQual',\n",
       " 'ExterCond',\n",
       " 'Foundation',\n",
       " 'BsmtQual',\n",
       " 'BsmtCond',\n",
       " 'BsmtExposure',\n",
       " 'BsmtFinType1',\n",
       " 'BsmtFinType2',\n",
       " 'Heating',\n",
       " 'HeatingQC',\n",
       " 'CentralAir',\n",
       " 'Electrical',\n",
       " 'KitchenQual',\n",
       " 'Functional',\n",
       " 'FireplaceQu',\n",
       " 'GarageType',\n",
       " 'GarageFinish',\n",
       " 'GarageQual',\n",
       " 'GarageCond',\n",
       " 'PavedDrive',\n",
       " 'PoolQC',\n",
       " 'Fence',\n",
       " 'MiscFeature',\n",
       " 'SaleType',\n",
       " 'SaleCondition']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qualitative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa2aa7142b0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "missing = train.isnull().sum()\n",
    "missing = missing[missing > 0]\n",
    "missing.sort_values(inplace=True)\n",
    "missing.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "19 attributes have missing values, 5 over 50% of all data. Most of times NA means lack of subject described by attribute, like missing pool, fence, no garage and basement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa2a6d12400>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = train['SalePrice']\n",
    "plt.figure(1); plt.title('Johnson SU')\n",
    "sns.distplot(y, kde=False, fit=stats.johnsonsu)\n",
    "plt.figure(2); plt.title('Normal')\n",
    "sns.distplot(y, kde=False, fit=stats.norm)\n",
    "plt.figure(3); plt.title('Log Normal')\n",
    "sns.distplot(y, kde=False, fit=stats.lognorm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is apparent that SalePrice doesn't follow normal distribution, so before performing regression it has to be transformed. While log transformation does pretty good job, best fit is unbounded Johnson distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "test_normality = lambda x: stats.shapiro(x.fillna(0))[1] < 0.01\n",
    "normal = pd.DataFrame(train[quantitative])\n",
    "normal = normal.apply(test_normality)\n",
    "print(not normal.any())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also none of quantitative variables has normal distribution so these should be transformed as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Spearman correlation** is better to work with in this case because it picks up relationships between variables even when they are nonlinear. OverallQual is main criterion in establishing house price. Neighborhood has big influence, partially it has some intrisinc value in itself, but also houses in certain regions tend to share same characteristics (confunding) what causes similar valuations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['MSZoning_E', 'Street_E', 'Alley_E', 'LotShape_E', 'LandContour_E', 'Utilities_E', 'LotConfig_E', 'LandSlope_E', 'Neighborhood_E', 'Condition1_E', 'Condition2_E', 'BldgType_E', 'HouseStyle_E', 'RoofStyle_E', 'RoofMatl_E', 'Exterior1st_E', 'Exterior2nd_E', 'MasVnrType_E', 'ExterQual_E', 'ExterCond_E', 'Foundation_E', 'BsmtQual_E', 'BsmtCond_E', 'BsmtExposure_E', 'BsmtFinType1_E', 'BsmtFinType2_E', 'Heating_E', 'HeatingQC_E', 'CentralAir_E', 'Electrical_E', 'KitchenQual_E', 'Functional_E', 'FireplaceQu_E', 'GarageType_E', 'GarageFinish_E', 'GarageQual_E', 'GarageCond_E', 'PavedDrive_E', 'PoolQC_E', 'Fence_E', 'MiscFeature_E', 'SaleType_E', 'SaleCondition_E']\n"
     ]
    }
   ],
   "source": [
    "def encode(frame, feature):\n",
    "    ordering = pd.DataFrame()\n",
    "    ordering['val'] = frame[feature].unique()\n",
    "    ordering.index = ordering.val\n",
    "    ordering['spmean'] = frame[[feature, 'SalePrice']].groupby(feature).mean()['SalePrice']\n",
    "    ordering = ordering.sort_values('spmean')\n",
    "    ordering['ordering'] = range(1, ordering.shape[0]+1)\n",
    "    ordering = ordering['ordering'].to_dict()\n",
    "    \n",
    "    for cat, o in ordering.items():\n",
    "        frame.loc[frame[feature] == cat, feature+'_E'] = o\n",
    "    \n",
    "qual_encoded = []\n",
    "for q in qualitative:  \n",
    "    encode(train, q)\n",
    "    qual_encoded.append(q+'_E')\n",
    "print(qual_encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [],
   "source": [
    "def spearman(frame, features):\n",
    "    spr = pd.DataFrame()\n",
    "    spr['feature'] = features\n",
    "    spr['spearman'] = [frame[f].corr(frame['SalePrice'], 'spearman') for f in features]\n",
    "    spr = spr.sort_values('spearman')\n",
    "    plt.figure(figsize=(6, 0.25*len(features)))\n",
    "    sns.barplot(data=spr, y='feature', x='spearman', orient='h')\n",
    "    \n",
    "features = quantitative + qual_encoded\n",
    "#spearman(train, features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa29aa708d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zwsEAaBg6hpPo24xrBvRzmSOmy3FQ0m1tqqQ7L0NCp49ooaF17JQsHTsxHelmy9CNy2LUCwC3DOkHppjRd27F9DXni+m25jM0dmZl/0ba91n0JO1PGUoKTxg/healdPuzWKG3AKam7GEe05bis99rfa6xW0C166sNalUWSaPRYPr06fDy8sLx48dx7NgxFBYWYs2aNdUqV6nU/e3gyZMnmDlzJubMmYNjx44hIiICbm5uQlg6h8Ph1DU0GpXWH32lVt19Fy9ehJGRkbDzWCKRIDAwEJ6enrhy5QqWLl2KNm3aAAACAgIwd+5ctGrVCl999RUSExOhVCqFQS48PBzHjx9HYWEh1Go1Nm3ahKlTpyI3NxdKpRIzZ86sMtJu79698PPzg6urq2Arj+l/9uwZAgMDkZKSAhMTEyxevBjOzs5Yt24dHj16hNTUVDx69Ajjxo0TMgtv3LgRERERsLa2hr29Pdq3b19b3cjhcDgvB1+TqprExMRKf7zNzc1hb2+Pvn374siRI2jTpg2ysrKQlZUFFxcXrF69Gj169MDSpUuRm5uLYcOGoVevXgCAmzdv4vDhw7CysoJSqcR3330Hc3NzZGdnY8SIEfD09ISI4apKTEyEn58feWzdunV46623sGHDBly4cAHz5s0TFCbu37+P3bt3Iz8/HwMHDsSoUaNw584dxMTEICIiAiqVCv7+/nyQ4nA4+geP7nt53nnnHSxatAgzZszAkSNHhFnNb7/9hlOnTmH79u0AyoRg09PTAQDvvvsurKzK8s1oNBqsXr0aV65cgVgsRmZmJp48eYKGDRvq3JarV68Ken89e/bEs2fPkJ+fD6Bsl7WhoSGsra1hbW2Np0+fIjY2Fl5eXsJaVrm0EofD4egVfCZVNa1bt8axY8cq2PLz85Geng4XFxdYWVnh9u3bOHLkCBYuXCicExoaipYtW1b43h9//FEhwCEqKgrZ2dkIDw+HVCqFh4cHSkoYK9D/35aEhASdN9++GOAhkUheaj2Mw+FwXgt1ILqvVgepnj17YuXKlYiIiICfnx9UKhWWLVsGf39/mJiYwNvbG1u3bkVeXh6cnZ0BAL1798aePXsQFBQEkUiEmzdv4q233qpUdl5eHho0aACpVIqLFy8iLS2tyrZ8+OGHGDZsGPr27YtOnToBAI4fP47OnTuja9euOHz4MKZNm4ZLly6hfv36MDendfMAoFu3bpg/fz4mT54MpVKJ06dPMyXuX2SGTzZpPxlen7Rvmknn5kzdkkLan+fS0WAPNHQ0oJThCkiX0o/FV4ubk/a0ZddIOwA8yqY113I1dITXpA70tf0ea0faWRFqgdMY0aNS+nzlrWTSbtCuOWnP2nmPtNvO60XXCwDG9P1JW3CatPtb0gnjzJ3peKfYGPp5aWhC69t160/r2Kme0C97imw6Qq1Lb1vSfm8PHSWIQvp5BAD/abplvE3dTms0WjSkoyxNWtHP9sETTqTdWEH/Rt4xpvUHG7XOJ+3PUuioVXv/6qeyqJI64O6r1eg+kUiE7777DkePHkW/fv3Qv39/GBkZYdasWQCA/v37IyYmBgMHDhS+M3XqVCiVSgwaNAg+Pj5Yu3YtWbavry9u3LgBX19fREZGVpp5/R0bGxusXr0aISEh6N+/PwYOHIjffvsNZmZmmD59OhISEuDr64tVq1Zh2bJlVZbVvn17eHt7QyaTYdKkSXBxcdGxZzgcDucVoEPSQ32l1tek7O3t8d///pc8ZmNjg5s3b1awGRsbY/HixZXOLc8WWY61tTUzc2R5ll0HBwdhjxRQtn/qhx9+qHS+iYkJNmzYUMn+6aefVvj/i2VNmTIFU6ZMIevncDgcvUCPBx9t4YoTHA6HU1epA+4+PkhxOBxOXYUHTnA4HA5Hb6nr7j6NRoPRo0fjk08+ETIyHjlyBGFhYWR+JV2YM2cOrl27hnr16kGj0SAwMBA9evSoVpm6sGbNGtSvXx/jx48HACgUCvTu3RujRo3CZ599Rn7n/Pnz2LNnD7l+1adPH0RHR8PCgh2t8/gsLT1iypiSK289IO3FRbRWWpGKvp1G0O1BlTB06VS3/iTthYVsHcZCDS1mZgb6Da8kh47lyRPR5YgZmm7Ku3S0p8iIju4rSKAj3Szb0PcsLYPOXtzo4UPSDgAaxh+MnOcMLcZSOsrOwoS+ZjMJQ+CQgaSJDWkvSKAj5qRmdL2ixnR039PCDNJuy4g2BICSi3TWYQNG5mRW35UU0/dZw7g9uxxoebSsNDpKUCLV8TfFOF+jrGU5ojrg7qsyuk8kEmHRokVYtmwZSkpKUFBQgDVr1lQ7d335XqPAwEBERkZi3rx5WLRoUbXKrC6//fYbWrVqhSNHjrzWdnA4HE6N8b8Q3de2bVu899572LJlCwoLCyGTydC0aVMcOnQIe/fuRWlpKVxdXREcHAyxWIygoCAkJCSgpKQEAwcOxPTp0wGUzTQGDRqE3377DZMnT65Qx9tvv43MzL/eoOLj47F8+XIUFhbC2toay5Ytg42NDUaNGoWOHTviypUrKC4uRkhICP773/8iMTERvr6+QjTeli1bBFmjESNGICCgTN13/fr1iIqKgrW1NWxtbVG//l/7k+RyOSZMmICdO3ciPj4eHTt2BAD88ssvWLp0KUxNTdG5c2fh/OzsbMyePRtZWVno0qULallMnsPhcHRHjwcfbdFqn9T06dMRFRWFs2fPYtKkSfjzzz/x888/Y9++fYiMjIRKpYJcLgcAzJ49G+Hh4YiMjMT58+dx9+5doZwGDRogIiKiwr4oADh79qygBKFQKPDNN98gNDQU4eHhGDRoUIW9UsbGxggPD8fQoUMxbdo0LFq0CFFRUdi/fz9yc3Pxxx9/ICoqCmFhYdi3bx9++OEH3LlzB/Hx8Th+/DgiIyOxefNmxMfHC2UWFRXh0qVLcHd3h4+PjxBqXlRUhODgYGzZsgXh4eHIysoSvhMaGoru3btDLpejT58+FY5xOByOXqDRaP/RU7QKnDA1NYW3tzdMTU1haGiI8+fP4/r164K6eXFxMezsyhQB5HI5wsLCoFQqkZWVhbt376J169YAAG9v7wrlfvPNN1ixYgUyMzNx4MABAEBSUhISExMxYcIEAIBarYat7V8+73KdvLZt26Jt27awsSnzqzs4OCAjIwNXr15Fv379YGxctsPby8sLsbGxKC4uFuzGxsYV9PZOnTqFd999F0ZGRhg4cCAGDx6M+fPn4+7du2jevDmaNm0KoGwDcUREBAAgNjYWmzdvFuowM9NtpzyHw+HUOnVAxk3r6D6xWAzxC4nEhgwZgn//+98VzklOTsbu3btx8OBBWFhYYM6cORX09P6eXDAwMBBeXl7YuXMnFixYgIMHD0Kj0cDJyYncdAv8paUnFosr6OqJRCKoVC+3CBkdHY0//vhDGLiys7Nx+fJlPvBwOJw3mzoQOPFSIeg9e/bEjBkzMHbsWFhbWyMnJwdFRUXIz8+HmZkZzM3NkZWVhd9++w1ubm7/WN64cePw008/4cKFC+jSpQsyMzOFdSGFQoEHDx4Ieaf+ia5duyI4OBgfffQRVCoVTp48iTVr1qCkpATBwcGYNGkSSktLcfr0aQQEBAguwjNnzkD6/7puBw4cgFwuR2BgIB48eICUlJRK6hVdu3ZFdHQ0Pv74Y5w6dUqr5ImPn9J6gPki2uv69DI9BX9SREc65Yno25lhQJfvWEq/ZZkwnmvFzSekPbuoEf0FAFJG9F22iI6+eppO99Hbdo9J++8ZtOr93RhaK01qQF9cq6ktSHvR6UTSXqCxJ+0pO+iINgCwsqej2rKUjUm7VEG/dCVE0Pf5mYqOsnxWRNtVW2mduRIlrSWpUNMRlq1yE0i7GnT04PNiOjoVAExv0VGW0vt05OJzFR1lWVBI95GFlC5/QirdR5+U0i+qDRX081tURNtzS+hrtilhZ3KuEerAmtRLDVJOTk6YPn06JkyYALVaDalUioULF8LFxQWtWrXCwIED0bhx4wqBBlUhEokwZcoUbN26FT179kRoaCiWLFmC/Px8qNVqTJgwQetBqmPHjvDx8cHQoUMBAKNGjYKTU5l4pJeXFwYNGoQGDRoIgRHHjh1Dr169hAGq/Lw1a9YgODgYixYtwqRJk4TAiUePysJzZ8yYgVmzZuHw4cPo3LkzGjVi/6HmcDic14IerzVpi0jDw9JeGZcaDybtaWr6rd/Vlp493M+g33RraiaVLaHL8e5GK5THX2QP0CpGbM5zxr6nNiZ5pL2eJf3GyZpJNZXSM1v2TIoup+iXJNJ+NZaeSTWr/5y0A+yZVOxNeibVxJi+huJS3WZSLGyNaJXyEiV9b5gzqRZPSfude/RMSipiv93bW9P3X2pMzyrvpNF1GDD2BrJmUoEi+vn6pNSatDcEXU49I9rOmkm5jmTPpOp9G8U8pi1FO+Zqfa7JhOXVrq824IoTHA6HU1f5X3X3cTgcDkf/0bxkMJk+wQepV4iJIb34W6SgpV2eP6MDJEwljLBSxvOoUtK3mXXzW6hpF8TzZNqd1MzuGe5m0G4RXUkuogMnnIzpvpMyvNVFpfQCtkJFv1lm7aVdmcWF9MJ5KUSkPS+/iqCAfEZQACO4xOEt2nV4+RrtasxhuGntVHTf5TBcUNbG9P030tAPWHEe3dfFDFdvsUaMYkawUCM1bRfRXQczEf1bkIjp+2xqRPeFu5JOGJljQN/ntoZ0H6UW0M9vqYguR5lGuzdrDD6T0p527dqhbdu20Gg0kEgkCAoK0jqwgsWtW7eQlZUl6AqGh4dj+fLlwr4qJycnLF++HGvXrkW3bt3Qqxc7a+qTJ0+wYMECpKenQ6lUokmTJtiyZQtSU1Ph7e2NFi3+iv46ePAgUlJSEBgYiISEBHz22WeYOHFita7lTaamBijO/wasAYpTC9RgCPqvv/6Kr7/+Gmq1GsOGDcPHH39c6ZyYmBisX78eIpEIzs7OWLVqVbXrfWWDlLGxsSBVdPbsWaxevRp79uypVpm3bt3CjRs3hEEKKNswHBwcXOG8mTNn/mNZoaGh6NWrF8aNGwcAuH37tnCsadOmQtvLsbKywoIFC3Dy5MnqXAKHw+HUHuqaiYtTqVRYvHgxduzYAVtbWwwdOhQeHh6CUANQtk928+bN+PHHH2FpaYmnT+mAGl15La80+fn5glp4VlYWxowZA5lMhg8++ACxsbEAyrLohoSEwMfHB+PHj0d8fDwCAgLg6emJkydPQqFQIDQ0FDExMZDJZIiJiWHWN3/+fBw9ehRAmWJFaGgo/P394evri6SkJKEd5aoZAODs7FzlNZSHsRsYcI8ph8PRU2pIYDY+Ph7NmjWDo6MjDA0N4ePjU+kF/cCBAxgzZgwsLcv2rjVoQLtQdeWV/YUtLi6GTCZDSUkJHj9+jF27dgEoU3vo3bs3pkyZApVKhaKisrDYwsJC9OjRA/PmzcO0adPw7bffYvv27UhKSsK8efPg6emJGTNm4MaNG8LMKTw8HDExMbh69SoAYOzYsYJ004vUr19fEMjdvn07vv76a4wZMwafffYZ9uzZg169emHw4MGC2/Dhw4eQyWQAgM6dO1dbBZ7D4XBeCToETuzfvx/79+8X/j9ixAiMGDECAJCZmVnhJd7W1raC/ilQNpMCgJEjR0KtVmP69Ono06dPNRpfxmtx98XFxWHevHmIjo6Gi4sLAgMDoVQq4eXlhXbt2gEApFKpcIFt27aFoaEhpFIp2rZti7Q0OlcQQLv7/k6/fv0AAB06dMDPP/8MAHBzc8OJEydw9uxZ/Prrr/D39xcUJih3H4fD4eg9OgROvDgovQwqlQoPHjzA999/j4yMDHz44YeIioqqMseeNrwWX5WrqytycnKQnZ2Nbt26Yc+ePThz5gzmz5+PCRMmwM/PD1KpFKL/j4h5UadPLBa/tEZfOeXqEn8vy8rKCr6+vvD19cXkyZNx5coVtG/fvlp1vciDYkbkD71HEhGoR9p7Ma7/uZguKFdCRxaJGUGCBhraC3zuMZ3czkjM9nuXMKKa6jOu4ZmEvobkbCvSbsIIaUyS0Buki+jm4EI2HWFpxLi0+ozMkLfF9D0GAJMM+pgzY2No/DW6v0/QQZ84UkgnpfwJAodnAAAgAElEQVRa3Jq0Z0gZm7xL6HugZkQ0ZufQz2kBY29xKeMeAID6OR2Eky+mv9SQEXFYoqbPz82nGzXRiX7xPXnXgbS3nEJvYP8llL6XrOfo6Lkm9AEAo5hHdKCG1qRsbW2RkfGX5FdmZmYF4e/yczp16gSpVApHR0c0b94cycnJgrrPy/Ja1qSSkpKgUqlgZWWFtLQ02NjYYPjw4Rg2bBgSEmgdMAozMzOtNPO04cKFC4KrMT8/Hw8fPoS9PR3qy+FwOG8EGrX2nypwcXFBcnIyUlJSoFAoIJfLK2SSAMrk5C5fvgygTKQ7OTkZjo6O1b6EV74mBZSlpQ8JCYFEIsHly5exbds2GBgYwNTUFCEhIVqX2b17d2zevBkymaxSIkVdSUhIwFdffQWJRAKNRoNhw4ahY8eOSE1NJc9//PgxhgwZgvz8fIjFYuzatQsxMTEwN2e/SXM4HM4rpYZmUgYGBhWEu4cMGYI2bdpg7dq16NChAzw9PeHm5oZz587B29sbEokEc+fOrZBY9mXh2n2vkCg7egL/lOXiMqBvTa/imnH3NVAy9M00tB/wkYR2lRhV8QjVlLuvoYpuE0ujLdWAbivL3VfI8Ckw3X0q+sAjxuZPADBhlOWsYOxUZRBtQjf2SOE90q6zu6+UvjdMd5+ELqeA0adVufscS+lOYrr7lAx3H+O5M2fMGDo60er1LHef/2f087VdR3efeRWDyKhHe5nHtKVg6TitzzX7Yle166sNePw0h8Ph1FW4LBKHw+Fw9JYacve9Tl7bIPUqZJLWrVsHU1PTCpJFHh4eCAsLg7U1W8onKSkJs2bNgkgkQmhoKORyOaKjo4XsxIsXL0anTp0QEBCArKwsIVX9lClTMGDAAGa5EoZbrAHjbaejmE7tkCzSLWOwJcM1VcRwodxluMp8Tekd5Ek5dOI5AJAyJHCMGW46e0ZfsNKQFDNSfrhIdNNEY3ksbR3oclhpKHo2ymHW8SyHDstLkNAhutmMqM+PDbNJ+2RjevPk3Rz64jxM6HJultKRlCz9uV71dHsu3rJnKxGkZNDfacz4Y3tdSvepOSMOoIThsmx2mU7Jcobxd0K+mm7PwAZ0YtCMJ3QEpI1VzQR+MeHafS/Pq5JJehlOnjyJ/v37Y+rUqYiLi8Mvv/yCQ4cOwdDQENnZ2Sgt/UukcuXKlXBxcalWfRwOh1Mr8JlUzfB3maTPPvsM+fn5UKlUWLhwIbp27QpXV1eMHDkSv/76Kxo2bIhZs2ZhxYoVePToEQIDA+Hm5obQ0FAUFxfj6tWr/xjtl5qaikmTJqFLly6Ii4uDra0tNmzYgEuXLmHXrl0Qi8W4cOECAgICUL9+fWGfVlUzMA6Hw9EralBg9nXx2uSIy0PSBwwYgC+//BJTp04F8JdMUmRkJCIjIwUNvXKZJLlcDjMzM0Em6bvvvkNoaCgMDQ0xY8YMeHt7IzIyEt7e3v/YhgcPHmDMmDGQy+WoV68ejh07Bnd3d4wcORLjx4/H999/j3fffRfp6eno378/Fi5cKOwDKGfOnDmQyWSQyWTIyWG7ejgcDueVo9Zo/9FT9MLdV1sySSKGD73c7uDgIJTfvn17shwzMzOEh4cjNjYWly5dwmeffYbZs2dj8OCyVPDc3cfhcPQVDSNE/01CLxK7UDJJtra2mD9/PiIiIgDgpWSSrKyskJubW8FWUFAguBbLywAAiUTCLEcikaB79+6YMWMGgoKCcPz48epdMIfD4bwK+EyqZvi7TJKdnR2GDx8OhUKBhIQE+Pn5aVXO32WSunbtis8//xyTJk2Cubk5jh8/DicnJ0gYG0Yp7t27B7FYjObNmwMoC85o3LixTtdXjnMjOprq6pOGpJ0VWWaQTvuZnxTRkU7PGJFxNmp6UDZV0/3T2Jt+kHMOszejFqvosgwZmVM1GkYm1Gb0Zss7yXTfNWiUz2wTRb1mdF8YNKEVREwf0JuL6zmy+8K6O/1OmB1Bf8fLKZO0ZyXT0YA3i2l7c0kRaTe1KCHtb+EZaS9liEzav0v3xcNo+nlRKtjvxs4uj0k74xFGwTU70m7GEKZsYElHzH5q2Ju0Pyuir+G9zrQSTUEmnaWYlSnYcVoL0l5j1IE1qdc2SL0KmSRvb2+MGTMGo0ePhkgkQoMGDfD111/r1M7CwkIsWbIEubm5kEgkaNasGRYvXqxTGRwOh/Na0OMZkra8tkHq1q1bpN3f3x/+/v6V7HFxccK/P/30U/KYlZUVfvrppwrHRo4ciZEjR1Yqz8HBQUjFAaDCXqoXy+/QoQP27dtHtvX7778n7RwOh6MPaPggxeFwOBy9pQ4ETvBBisPhcOoqfCbF4XA4HL2FD1Kvhy+++AK//PILGjRoUGFd6e9cunQJUqlU0ARct24dDhw4IKhGuLm5Yc6cOQgICMDcuXPJ/U6nT5/G2rVroVaroVQqMXbsWIwcOZJZVk2ScI/O/tnImI5QUjB2FJQwNPqsVXTkjynjuVblsSLX6Ky2AMCoGrlqOgpKCrpyIwvabVHfsJi0Fzw3Iu0aRsZW04Z0NKDoMa2tlquio/6eJLL7oiFDi1EJRs4dRqRjCSPKjqXFmK+k+zo/h85eXFhCn69U08+XKpeOQk0ypMtpxrguAFAz8nho6EBEGIl0i14rKqLbpGCUw4oDLmYIKxYX0uWzolxrW1uvLmRieiMHqcGDB+PDDz/EvHnzqjzv8uXLMDU1rSBcO378+ApBElWhUCgQFBSEsLAw2NnZQaFQVEiCqEtZHA6H88rhM6nXQ7du3SplzN29ezf27dsHiUSC1q1bY/bs2di3bx/EYjEOHz6MoKAgrcp2dXXFiBEjcP78eQQFBQn7t4Cyzb8tW7as8evhcDicWoEPUvrD5s2bcerUKRgaGiI3NxcWFhYYOXJkhVQdFy5cwM6dO3H48GEAZbp7bm5uFcopLCxEx44dMX/+fABlqT3ee+899OzZE3379sUHH3wAsbjM7fFPZXE4HM7rRMPIvv0mUWcGKScnJ8yZMweenp7w8vJinvdPLjqJRIL+/fsL///6669x584dXLhwAdu3b8f58+exbNkyrcricDic18qbP0bph3ZfTbB582aMHj0aN2/exNChQ6FU0rIo/4SRkVEl2SQnJyeMHz8e27dvx7Fjx2qiuRwOh1PraNQarT/6Sp2YSanVaqSnp6NHjx7o0qUL5HI5CgsLYWZmhvx83TTcXqSgoAA3btxA9+7dAQC3b99GkyZNXrq8S09pnTlLhoaeiBHplltCR649kdC305wRQVTAuP25Evrd5e4ZWhsuqYroPisNfW2FYrqOd+xpvbrddxxJe1Ml3UeWuXS9pYzMrAm/09lonY1zSXs+I+MwK3oMAJJi6VxkuWI68mvXfQfS3ltFa/G1ZITAZYrpTMuWjCi+RDWd+dmCEQ2acoXOOtuolD7/Tg4jmhGAZQ79cmkkpu9nuoi+NlYW4VIlrW8Z8g3d1ynLfiftBkb0td1+Rl+bAeO3nBdBK+8AgOlM5iHt0ePBR1veyEFq1qxZuHz5MnJyctCnTx9MnToVkZGRyM/Ph0ajwdixY2FhYYH33nsPM2bMwMmTJ7UOnHgRjUaDrVu3Ijg4GMbGxjAxMcHSpUtr4Yo4HA6nFqgD7r43cpBavXp1JRulz9eiRQtERUUJ/+/atStZ3osafC9qBJqbm2PLli3kd/6uH8jhcDj6hj678bTljRykOBwOh/PPaBju8DcJPkhxOBxOXYW7+zgcDoejr9SBnIfVG6RcXV0rrOFUlxMnTmDt2rUoLS2FgYEBpk+fjgEDBrxUWampqfjkk08QHR2NS5cuYerUqXBwKIvgqV+/Pnbu3Ikff/wRJiYmVWb+LSoqwpdffok///wTGo0G9erVw9atW2FmZoZ27dqhbdu2wrnfffedUAdFhgEdcSTVMDLhgtalSwOtufaMIQ9mxNBKK2BE2JkwogEvSmi9OkZwGgAgj5EF2VJFuyFOZ9KZVm0ZXosG6lLS/ocRHfXFQsooP7vUkrSXMoL4stR0BCQAKBkbPjQMuyUjy8I9ER2hJhHRFyFmXNtNDX0/SxntUTOU7DKUdB8pGX9dHEvZ20POGdORq9aMvmhTSutJZkroG1TAiPpTpz4i7cnZdNTniSK6nHaMyFgFQ64whxG1CgAfMo/owP/6IFWT3L59GyEhIdi+fTscHR2RkpKCCRMmwMHBAR06dKh2+V27dsWmTZsq2EaNGvWP39u9ezdsbGywatUqAGXp5KXSsh+AsbExIiMjq902DofDqQ3qwkyqxjfzpqamYuzYsfD19cW4cePw6NEjqFQqeHh4QKPRIDc3F+3atcOVK1cAAGPGjEFycjK2bduGyZMnw9Gx7M3C0dERkydPxo4dOwAAAQEBuH79OgAgOzsbHh4eQn2jR48WMvpeu3ZN67auW7cO27ZtE8pfsWIFhg4div79+yM2NhYA8PjxY9ja2grfadmyJQwNdXtL53A4nNeBRqn9R1+p8UFqyZIl8Pf3R1RUFHx9fbFkyRJIJBK0aNECd+/exdWrV/HWW28hNjYWCoUC6enpaN68Oe7evVtpxuTi4oK7d+9WWV+DBg2wY8cOHDp0CGvWrMGSJUvI82JjYyGTySCTybBx40byHJVKhbCwMAQGBmL9+vUAgCFDhmDLli0YMWIE1qxZg+TkZOH84uJiocxp06bp0EscDodT+2jU2n/+iV9//RX9+/fH+++/j82bN1c6/uOPP8LX1xcymQyjRo36x7/d2lLj7r64uDisW7cOACCTybBixQoAZe62K1euIDU1FZMnT8aBAwfQrVs3MoeTLiiVSixevBi3b9+GWCyuMIi8COXu+zvvv/8+AKB9+/ZIS0sDALRr1w4nTpzAuXPncP78eQwdOhT79+9Hq1atuLuPw+HoNTXl7lOpVFi8eDF27NgBW1tbDB06FB4eHmjdurVwjq+vr7CEcvLkSSxdulTwVFWHV6bd161bN1y9ehXXr1+Hu7s78vLycPnyZWGDbatWrXDjxo0K37lx44Ywu5JIJEICL4Xir8XSnTt3wsbGBpGRkfjpp59QWkovpGtDuRtPLBZDpfprpdbMzAz9+vXDwoULMWjQIJw5c+al6+BwOJxXhkak/acK4uPj0axZMzg6OsLQ0BA+Pj44efJkhXPMzf8KxCkqKoKIEaSiKzU+k3J1dYVcLoefnx+ioqKEQahjx46YO3cuHBwcYGRkBGdnZ+zfv1+Y3UycOBEzZ85Ejx494ODggNTUVOzatQtr164FADRp0gQ3btxAx44dcfToUaG+vLw82NnZQSwW49ChQxUGl5rg6tWraN26NSwtLaFQKHD37l288847L1VWX9AZTB+pae07DeMmu1jkkHZJLh2JpGaU01RNRw/maujIqIC+6aT98nEb0g4AxiL6frDq6Ng0i7Q/zqAj0RSMiLP+UjqjrkJJn29lTevhWXchzfgtugFp79SSbj8AmNjRfXHxvD3dJjH9wlXfnG7rg1w6spD1JtrW9ilpT06nnyNjCd3+dp7PSfuhE/R1tbSk9RABoLMdnb1YYkSHKMbH09GgDRh/BxoxokrvbaXrbW5N34PAjrROYspl+jnNKaIjcruFVs+T9E/oMpPav38/9u/fL/x/xIgRGDFiBAAgMzMTdnZ/9bWtrS3i4+MrlbF3717s2LEDpaWl2LVr18s3/AWqNUgVFRWhT58+wv8nTJiAoKAgfPHFF9i2bRusra0FrTtDQ0PY2dnh7bffBlDmfpPL5UIId7t27TBnzhxMmTIFCoUCaWlp2LVrl5Bk8F//+hf+/e9/48CBA3B3dxfqHD16ND799FNERETAzc0NpqZssdOXISUlBQsXLgRQJmTr7u5eIZUHh8Ph6CsatfazmRcHpZdlzJgxGDNmDKKiorBx40aEhIRUqzwAEGnKfWh6xsqVK/HHH39g27ZtdSaaLq6pjLQ/KqEHVisR/RbXwIJ+67up40zKlqGazZrl9O5HzxJe60xKRc+MLEzoa2POpOr/D86k7GtoJvW+bjOpHmbZjBYBVjU0k1Ix1O4lDDXyRqZ0vUbG9D1o9ApmUibDg5nHtCWtp4fW5za5cIp5LC4uDuvXrxfWmMo9YJMnTybPV6vVwhJPddGbfVJ/Z86cOa+7CRwOh/NGU1OBEy4uLkhOTkZKSgpsbW0hl8uFvaPlJCcno3nz5gCAX375Bc2aNauRuvV2kOJwOBxO9dDF3VcVBgYGCA4OxkcffQSVSoUhQ4agTZs2WLt2LTp06ABPT0/s2bMHFy5cgIGBASwsLGrE1QfosbuvLnLefghpvyWhZW6aMyIVSxiuDFPQ7phnItq1JmXc+vpiWmrG2IC94y9dQbssixjSS88l9DUYM978WmhoF1e+mr42A4YejFRE2w3FtJ3140jV0PeMJWUFsF2QWQV037V/i04Aef8O7Wq8ZECX066Evm+sPjJmJBgUM2SXjKV0+ckltOsLYCclbC6m3W4GErqtmSX0fTDU8dpYUmMs2S3Wc5QqpmWdTBgpMzpY0y5XAGh9s/pZwB929dT63KaxJ//5pNdAjYWgt2vXDjKZDIMGDdJZ+YHFrVu3KoR7h4eHo0ePHsIG2rlz51b5/UuXLgk+0/DwcCxevBhAmdKEm5sbZDIZBgwYgP/85z9QM/Tqyjlx4kSFzWkvKmD8r8MaoDgcCtYAxal5NGqR1h99pcbcfS9ubD179ixWr16NPXv2VKvMW7du4caNGxWi+by9vREcXP0FxfHjx2PixIlQq9UYM2YMLl++jB49ejDPP3HiBPr27Vth8xqHw+HoM/o8+GhLrWzmzc/Ph4VFWaRRVlYWxowZA5lMhg8++EDQxHN1dUVISAh8fHwwfvx4xMfHIyAgAJ6enjh58iQUCgVCQ0MRExMDmUyGmJgYZn0sXT9tKC0tRUlJCSwty5ScDxw4gCFDhmDQoEH49NNPUVRUhGvXruHUqVNYvnw5ZDIZHj58CAA4evRoJa0/DofD0RfUKpHWH32lxmZS5Tp2JSUlePz4sbCRKzo6Gr1798aUKVOgUqlQVFS2tlBYWIgePXpg3rx5mDZtGr799lts374dSUlJmDdvHjw9PTFjxgzcuHFDmDmFh4cjJiZGCGscO3Yshgyh13n+iZ07d+Lw4cN49OgR+vTpg3bt2gEok0YaPnw4AGDNmjUICwtDQEAAPDw80Ldv3wqpQ8q1/s6cOYP169dj586dL9UWDofDqQ00/6Ak8SZQK+6+uLg4zJs3D9HR0XBxcUFgYCCUSiW8vLyEwUAqlQobgdu2bQtDQ0NIpVK0bdtW0M2jqGl3X2lpKWbMmAG5XA4fHx8kJibi22+/RV5eHgoKCtC7d29mGZTWH4fD4egLdSFVR62EoLu6uiInJwfZ2dno1q0b9uzZgzNnzmD+/PmYMGEC/Pz8IJVKBW0nsVjM1M3TBpaunzZIpVK4ubnhypUr8PHxwfz587FhwwY4OzsjPDwcly9fZn5X1zZnaejIn/pK+kk6a0zfnneL6WgqlkTQfUPaq9taQbe5QE3Xe1dNR1IxTgcAmDOimlhJD/PE9JtfhpruO3NGFBcr6R0rASArjC+XEYXI2FuKeDHdRwAgVdDHHEHfz/ib9EbVZCl9baw25YtYEZb0jWukpJ8LVsBDLiObo5qRDLO0ipf7FCXdR88ZKxP1GDdUysgkWcRolPcM2r5vIytRKWlm5hjMkTISjz5ryPgGUBOr3+o6MJOqlTWppKQkqFQqWFlZIS0tDTY2Nhg+fDiGDRuGhIQErcsxMzNDQQGtwfYi5bp+ACro+mmDRqPBtWvX0LRpUwBAQUEBGjZsiNLSUkRFRencFg6Hw9EXNBqR1h99pcbXpICyP/whISGQSCS4fPkytm3bBgMDA5iamuq0wat79+7YvHkzZDIZU34DYOv6VUX5mpRSqYSTkxNGjx4NAJg5cyaGDRsGa2trdOrUSRiYvL29ERQUhO+//x6hoaFaXwOHw+G8LvQ5IEJb+GbeV0iE3WjSznIRxBvRDxjL3adhbPJNMKLfRVjuPmOGIzvdgHYzVeX2Zrn7WLDcfZYquhZzRlufSmj3DdPdx0BXd19hFb4JlovIsZS+nyKGD5Ll7mP9PWpUSvfRcwndWJ3dfYxyWM9FVe6+BgzXN6ut9RjPF2ujugHD3ncG3af7NjI2hevo7mM9F5ZVrBKMeVS9LTwAcLOVj9bnvpUkr3Z9tQGXReJwOJw6Sl1Yk+KDFIfD4dRR9HmtSVveuEGqXbt2Qg4qAPjuu+/g4ODAPN/DwwNhYWGwtraGq6sr4uLikJqaCm9vb7Ro0QIajQampqb45ptvhNxVFKmpqYiLi4Ovry+Asj1bL+7h0obODrQW2/WURqT9370z6LZcoGWIUgtorbSWCtoJoWa4B1MZbr1ho+ikjQk/MMK4wNbWSzSk7bJmdCj/5bt02occMV33+51SSbuGIT+oLGK4k96pR9r/+IFOH+PizU7oJ7am788fu3SLX/J3oZ+LG9fo56iQEfXp9hbd139epyPO6hnT2oPNPWm9wvOMdCZmIrYGZPuej0m7xJz+UxX/c33SLmVoMbL+ZKtT6ESi3Ri/hfoNaY3B0mK6r589o6MWnaZZMlpUM9SFxZw3bpB6cT9WdWjatKlQzr59+7Bp06YqgzrS0tIQHR0tDFIcDoej79QFd1+thKC/al4UjwXKEnFdunRJ6++/KOOUmpqK0aNHw9/fv4JQ7qpVqxAbGwuZTCYoS2RlZWHixIno168fli9fXnMXxOFwODWAWi3S+qOvvHEzqRdD3R0cHPDdd9+9VDkPHz6ETCZDQUEBiouLceDAAQBAgwYNsGPHDhgZGSE5ORmzZs1CeHg4Zs+eje3btwsZKcPDw3Hr1i1ERETA0NAQAwYMQEBAAOztabcUh8PhvGrqwkzqjRukasPdFxMTg6CgIGzbtg1KpRKLFy/G7du3IRaLkZyczCyjZ8+eqFevbM2iVatWSEtL44MUh8PRG+pC4ESdcPdJJJIK+aBKSugFXhYeHh6CivnOnTthY2ODyMhI/PTTTyhlJB4E/pJFKm+DrnJOHA6HU5uoNSKtP/rKGzeTomjSpAl+/PFHqNVqZGZmIj4+XqfvX716VZBFysvLg52dHcRiMQ4dOiQMPDUhi/TsCR2Vp2RskixOoSOUcotoHbs8RqSbhBHiI2VsFmVtwH0kpwf/pyor0g4ARow6misYG1gZu22fGNDvU3al9ItB8nU66ov1ZpmjpPvUIZ2OaGRt2nx0hv2T0mjoKLg8jQVpfyClyyplRPGxtBtZLUq8YUPaDSV0nxYq6Ei3B6cYFTD404DOggsADRPMSLuBAd2mXA3dJjMV/XxJGdmFU0/Sz5ehIWPDuxVdfmI8HdHI2mj/LJqOQgUAswXMQ1pTB4L76sYg1aVLFzRp0gTe3t5o1aoV2rdv/4/fKV+T0mg0kEqlWLJkCQBg9OjR+PTTTxEREQE3NzeYmpYNLE5OThCLxRg0aBAGDx4sBFpwOByOvqJSv/nOMi6L9AqJb06Hr98vpvfidG/7iLTfS6Tf1tJAv6HqOpMqZqhmd7J6StrvZus+kyplvFm+7UTvAZLfo/fCsWZSttIi0q7zTMqcnkk9LaD3vTS0oPfPlNVN2x/k6jaTciqlZ7SsmRQLczHtymbtMVIx+s7MiC4nrZCeFaUxrgsA3jXNJu2smdSdp9Z0mxjK8qyZlHU9+r6xMt03aEZ7Va7H25J21kzKuSW9LwwAmlzQcYpKcNZuqNbnumWEVbu+2qBOzKQ4HA6HUxnW4PgmwQcpDofDqaPoqO+sl/BBisPhcOooLOmzNwmtBqlyvTyNRgOJRIKgoCB07txZ60rWrVsHU1NTTJw48aUb+jLcunULfn5+2LJli5CqPjU1FZ988gmio6O1LqegoADLly/HuXPnUK9ePYhEIowcORLDhw/XqT3Pi+l1DxYbHjYh7b3UtH8+m5H904oRGc9KZ8BKT3Erh/b/P5GyF2ftGWkoshlZYbMe0utz5oxMvk8N6HWYQhW9HsLCkLFWEVdERwmqGMs/D/LZkWsMeUBYMCIanzPquC6inyNLRshhIyV9D+6J6HU1Y8brtynjuUsqoaNWWUtkrDQXAJDxnL5v6RJaK5ER9IlC0OcXMVLBdOhFa/fd/4VuT246fZ8fGtD1smiYzn5O6V+/bvzPuPte3EB79uxZrF69Gnv2VD/XiVKphIFB7U3moqOj0aVLF8jlcmGQehm+/PJLODo64vjx4xCLxcjOzkZYWOVFxtq+Hg6Hw9EF1f/KIPUiL+rcAcDWrVtx5MgRKBQKvP/++5gxYwYAYOPGjYiIiIC1tTXs7e2FsPCAgAA4Ozvj6tWr+OCDD9CvXz8EBgYiJycH1tbWWLp0KRo3bozU1FTSPn/+fBgZGeHWrVt4+vQpvvnmG0REROD3339Hp06dsGzZMgBl2YGPHj2KHTt2YPTo0SgpKYGRUdkbqFKpxOzZs3Hz5k20adMGISEhuHLlCsLCwoSsu5cuXcL27duxYMECxMfHY9WqVRCLy17brK2t8fHHHwvnrV27FhYWFrh//z6OHTv2sveCw+FwapSqEpK+KWgVRF+ulzdgwAB8+eWXmDp1KgDgt99+w4MHDxAWFobIyEgkJCTgypUruHHjBmJiYhAREYEtW7bg+vXrFcorLS1FeHg4/vWvf2HJkiXw9/dHVFQUfH19hf1KLDsA5ObmYv/+/fjiiy8wZcoUjB8/HnK5HH/++Sdu3boFALh27RocHBzQtGlTdO/eHb/88ovw/fv372P06NE4cuQIzMzM8MMPP6BXr16Ij49HYWFZKGpMTAy8vb2RmJgIZ2dnYYCiuHnzJhYsWMAHKA6Ho1eodfjoK1oNUuXuvqNHj2Lr1q2YN28eNBoNzp07h3PnzsHPzw/+/v64d+8ekpOTERsbCy8vL5iYmMDc3PUqexAAACAASURBVBweHh4VyvP29hb+HRcXhw8++AAAIJPJcPXq1SrtAPDee+9BJBLByckJNjY2wkbb1q1bIy2tLEeOXC6Hj4+PUJ9c/ldqZHt7e3Tp0gUAMGjQIFy9ehUGBgZwc3PD6dOnoVQqcebMGXh6elbqi40bN0Imk6F3796CzcXFBY6Ojtp0JYfD4bwyNBBp/dFXdHb3ubq6IicnB9nZ2dBoNPj4448xcuTICueUp7JgYWJCL9hqS7lmnkgkqqCfJxaLoVQqoVKpcPz4cZw8eRL//e9/odFo8OzZM+Tn5wvfe5Hy/3t7e2Pv3r2wtLREhw4dYG5ujtatW+P27dtQq9UQi8WYMmUKpkyZAldXV+H75aoUHA6Ho0/ocQYOrdF5kEpKSoJKpYKVlRV69+6NtWvXwtfXF2ZmZsjMzISBgQG6deuG+fPnY/LkyVAqlTh9+jRGjBhBlufq6gq5XA4/Pz9ERUWha9euVdq14cKFC3BycsK2bdsE27x583DixAl07doVjx49QlxcHFxdXYXgCgB45513sGDBAhw4cECY7TVr1gwdOnTAt99+i5kzZ0IikaCkpAQvI9Rx2ZCOCGrByJzbgPGEJRrSt60+I4qPNV3OkbC0/ujzbxnpLrGiZDxiXzyn8319a9GdtDOCslDKsD83oA+wfrSmjAMZjM5oyag4sYrQNVplDmDli7Vk3E9zhm/GRE0fYClXpBnQbc1mqDVYM/9c0OW0VdDXVVLFH84TJnQvGTK6tRHjvuUyHtV8RhTnqjN2mO1eWe3kcTH9Qh2vpFVCVIxrY+UilmvYmXk7Mo9oT02GoP/666/4+uuvoVarMWzYMGFdvhyFQoG5c+ciISEBVlZWWLNmTZVZ07VFq0HqxRxOGo0GISEhkEgk6N27N5KSkoSZlKmpKVasWIH27dvD29sbMpkM1tbWcHFxYZYdFBSEL774Atu2bRMCJKqya4NcLoeXl1cFW79+/fDjjz+ia9euaNGiBfbu3YvAwEC0bt0ao0aNAlCmZN63b18cOnSoQpber7/+GsuXL8f7778PKysrGBsb4/PPP9e6PRwOR/+x3HWystGWfrl+U6ipvAwqlQqLFy/Gjh07YGtri6FDh8LDwwOtW7cWzjl48CAsLCzw888/Qy6XY+XKlfj222+rXTfX7nuFrGr6IWlnzaRSDOnXQca2GuZMyohxi1l7RliLqBkvEV3PapPOMynGNZew9skwXiDZMyna/ipmUo0Yr9+s+6DrTOoxYzNRzc2kaFgzKdZ+MQB4YEBfgyFDN7CmZlIAsDh5byXbz4xBKt2AvpusmT1rJpVdhdziFw+qv80nzH6M1ucOTa98/eXExcVh/fr1gneqPPnr5MmThXMmTpyI6dOnw9XVFUqlEu+++y4uXrxYaXlFV/imHg6Hw6mj6DID2b9/P/bv3y/8f8SIEcIyTWZmJuzs7IRjtra2lVIiZWZmCklfDQwMUK9ePWELUXXggxSHw+HUUXQJLX9xUNIn3vxkIxwOh8MhUYu0/1SFra0tMjL+CizJzMyEra1tpXPS09MBlAkm5OXloX59WlZMF7SaSeXk5GD8+PEAgCdPnkAsFgtTuIMHD1YIAweAZ8+e4ciRI0JAwoMHD+Dr64sWLVpAoVCgU6dOWLJkSY1JCE2ePBm5ubn48ccfBducOXMwYMCASgEUVXHmzBmEhoaioKAARkZGaNmyJebNm1dhmkuhVCrRo0cPIQU9i3eVdM6apxpaiy1NTC/oDFHQ+YTugpX5l26PDUPTLYtxXz60o/NbXX3A7h9zDX0NofXotade1lmkPeMJren3mJEHylFK5/tRKOlFgPoWdP4pCxs6m27sXfqah4jzSTsAmJnT9y3+MZ0hl0UjEV3OM0aWWsdS+n26u4R+HlNKaD05Yw39vLzFyIl0+kFj0v6uEa2TBwDujEy4Boy1Klbf2TDWQViBBPXUKsTYjqxkb239nDy/50Bao+/Pg4x8ZQo6srfHv2o3RrymZJFcXFyQnJyMlJQU2NraQi6XY9WqVRXO8fDwwKFDh+Dq6opjx46hR48e1V6PArQcpOrXry9o92kjFvv8+XPs27dPGKQAoEWLFoiMjIRSqcT48eNx/PjxCpt6X5Znz57hzp07MDIywqNHj9C4Mf3D+Cdu376Nb775Bhs3bkTLli2h0Whw8uRJpKWlVRqkuEYfh8N5E6ipfVIGBgYIDg7GRx99BJVKhSFDhqBNmzZYu3YtOnToAE9PTwwdOhSff/453n//fVhaWmLNmjU1U3d1C9iyZYswgI0YMQIBAQFYtWoV7t+/D5lMBjc3NwwbNuyvCg0M4OLigszMTABlM7EzZ84gPz8fDx48wKRJk1BYWIjo6GgYGxtj8+bNsLCwwM6dO3HgwAFIJBI4OTlh5cqVAIBjx47By8sL5ubmkMvlmDRpklDX2bNnsWHDBhQUFCAwMBDu7u4YPHgwVq5ciZYtWwIARo0aheDgYGzZsgVTpkwR7CKRqMIsbNSoUXBxcUFsbCwGDRoEDw8PzJ49G8XFxZUUNTgcDkcfqEm5I3d3d7i7u1ewzZw5U/i3kZGRoH1ak1RrTeqPP/5AVFQUwsLCsG/fPvzwww+4c+cOZs+eLcyc5syZU+E7xcXFuH79egVZocTERGzYsAEHDx7EypUrYWlpiYiICLRv3x6HDx8GUCZkGxERgaioKAQHBwvfjY6Oho+PDz744IMK0kcAkJGRgbCwMGzcuBHBwcFQKBTw9vbGkSNHhOPPnz9Hu3btkJiYKIjgslCr1QgPD8f48ePx1VdfYezYsYiKiqp29AqHw+HUBhodPvpKtQapq1evol+/fjA2Noa5uTm8vLyY6zLlM6tevXqhcePGaNOmjXCsR48eMDU1hY2NDUxNTfHee+8BANq2bSto8bVu3Rqff/45Dh8+LLjaMjMzkZ6eDldXV7Ru3RpqtRpJSUlCuQMHDoRYLEbLli1hb2+P5ORkDBw4EEePHgVQJiI7YMCASm19+vQpZDIZ+vXrV0HiaeDAgcK/f//9d8FdWb7RmcPhcPSJmgqceJ28sui+8pnViRMnEBcXhzNnzgjH/q6/V/7/ci0+ANi2bRtGjhyJ69evY9iwYVCpVDhy5AhycnLg4eEBDw8PpKenV5pNvYhIJEKTJk1gZmaGu3fvCkrnANCmTRskJCQAABo0aIDIyEgMHTpUUEUHKmv01cSiIIfD4dQWdUEFvVprUl27dq2wmHby5EmsWbMGZmZmKCigo6usra0xa9YsbNq0qZJ/k4VKpUJGRgZ69uyJLl26oG/fvigqKkJ0dDR27NiBjh3LVK6Sk5MxefJkIafV0aNHIZPJkJycjPT0dDRr1gxA2Yxo06ZNUCgUgqzHRx99hH//+9/o2LGjsC5VVFQEqZSOmHr77bdx5MgR+Pj4ICoqSqvrMDUsJe0PSmh9sHaM7etmxgrSbldAtzVDQtuNGY+ms4aO+pKa0rFRRhr2I17KiC5qb5BH2lMf01pmpgZ039VX0vaiUoYiAEO5QM1QfSjJp38izKzGRezsy6Zm9H0zZvRfYxP6N5RaREffPWdoMdZX0fetpJS+tham9L0pKKYj2pQM2Q/WK1xiAVuv7h37dNKuYvwWGoLuU0ZyYRhL6L74TUr3qdUz2q6IYERYKugIW2MRXa86m/6t1RQsLcE3iWoNUh07doSPjw+GDh0KoCy4wMnJCQDQvn17+Pr6wt3dvULgBAAMGDAA69evx++//65VPSqVCrNnz0ZBQQE0Gg3+9a9/ITs7G48fP/4/9s47LKrj6+PfXWAREKUJKhpb7IqioGIwKooVFBFLsJdYULH3WKJYY+y/oMYoxlhjEARsQTTGRtRg7wiiSBGRXpbdve8fvHuzy84sexGQkPk8zz7P7tzZabecO2fOnKPmF7B+/fqQSCR48OABgEK7/cGDByM7OxurVq3iZ2h9+vTB+vXreWEGAC1atMCiRYswb948ZGdnw9zcHLa2tmp5VPnmm28wb9487Nq1ixlOMBiMCklFniHpCvPdV47cq+9OTH+cT/aonEPxredomEZMT8omv/XRZlK15OS3UGM98n6YBk3fE9OjHtUipmvDxpC8LylTSn5bp82kcmTkvulRloJpMymbauRZi5EJud4nCZbEdFMRzUsbUNOKPEN5/o684bGsZ1LmeuTzT5up02ZSNWtmENOvJJL3klWl+BgEgA6fC5tJvXpFNlqi1UCdSemRx7RjAWX/nBF5JpWcTZ5JGYrILbLzos+kTHeeph7TlZ11yf5CSUx//fG+AssCttmHwWAwKimVYQbChBSDwWBUUiqy1Z6uMCFVjtRqTFaLxNwnu/zpP5rskifuOPn9SEJZnDVRkNVA2SJy+hM9slrHrj9ZtfJlf+DpdrJrnPR8clkvCqqSy2r+hph+47EtMT1TTF60HzSJ4gCHYggj/ZvcfsNODYnpuXs0A+QBQP0V9sR0AADNgGHVfWJ6FWOyqrGLB7n4e0fJRhvGhuRyPvcgtyfnPlktaU2xBTbpbENMb3GAXA4ApOST3QSZNCU/kvRqk1Wi+n+Q3WhxAp/OYa/Iajd7BVkVT1Ott3UgXxdpr8j9FUnohjalQWVYkyoXE/SUlBTMnTsXPXr0gKenJ4YNG4bff/+9PKpW4/nz5+jduzfy8v55+E+aNAmhoaEaeSMjI9G+fXsMHDgQ7u7uGDt2LN6/L1yTCQwMxKpVqwAA4eHhePHiRfl0oIJCE1AMBgmagGKUPnIBn4pKmQspjuMwbdo0ODg44MKFCwgMDMTmzZvVPOpqQ0ZxgloSGjduDFdXV/j7+wMoFDAymQxubm7EOh0cHBAcHIyQkBC0bt0ahw5pBgVjQorBYFRUKsNm3jJX9924cQMGBgZqzmZtbW0xatQovHnzBgsWLEBubqEFzbJly9CuXTtERkZi27ZtqFatGmJiYnDu3Dn4+PggMTER+fn5GD16NB/35Ndff8XevXthamqKZs2aQSKRYPny5UhNTcWKFSvw9m2h5+4lS5agffv2mDZtGjw8PNCnTx9s2rQJu3btAlDoODcuLg6vX79G7dq11eKqcByH7OxsfPbZZ2p9+/vvvxEREYG//voL/v7+2LFjh0YeBoPB+FRUBnVfmQup58+fo0WLFsRjlpaW2L9/PwwNDREbG4s5c+YgMDAQAPDo0SOEhISgbt26AIC1a9fCzMwMeXl58PLyQq9evSCVSuHv74/AwECYmJhgzJgxaNasGQBgzZo1GDNmDBwcHPD27VtMmDABZ86cgZGRERYuXIgRI0Zg3LhxqF+/Pt+e6OhoHD58GFWqVEFkZCRu3bqFgQMHIi0tDUZGRpgzZ45a+9u1awcXFxd069aN6F6JwWAwPiXMuq8EfPvtt7h9+zYMDAwQEBCAVatW4cmTJxCLxYiNjeXztW7dmhdQAHDw4EF+HSshIQGvXr1CSkoKHB0dYWZmBqBwk66yjGvXrqmp4bKyspCdnQ0TExO4uLigWrVq8Pb2Vmubi4sLqlT5R1/u4OCA3bt3AwD27NmDjRs38mtRDAaDUdFRVAIxVeZCqnHjxjh//jz/e8WKFUhNTYWXlxcCAgJgZWWF4OBgKBQK3r0RoO4nLzIyEteuXcOxY8dgZGSEUaNGIT+fvJlOiUKhwPHjx2FoSLaeEYvFGr73jIzI7okAoEePHpgxY4bWOosj+x3Z0i2LYqGWFEILoEdx1SMmT+5zKQ5qbBW0DbKUpUqKdVqOlGwxBwDZlLbacOQNo/mZ5Py2+mTrq+QC8iJ83k3yplCaByd9M3KfuQzyOaBZj8mu3CZXAEBcg2wplicl99moKnmMcu6SrURzOErwSXIxyH1I3hSe9U6YYYOxlLxuTLPstK1KDwyZ85x8TRqDHCjx1QuyxalYRH441/6MHMTw1DKytid6HXm92ciIcv1+IF9HMso9JW7WiJheWlRkgwhdKXPDiU6dOiE/Px+HDx/m05TWdZmZmahRowbEYjGCg4MhpzwEMzMzUb16dRgZGSE6Opp3p9S6dWvcvHkT6enpkMlkasLQ2dkZBw8e5H8/fvz4o/px+/Zt4nqTNj+FDAaD8Sn5zzuY1QWRSIT//e9/WLduHfbu3QsLCwsYGRlh3rx5aNGiBWbMmIGgoCB06dJFw8u4ki+//BJHjx5F37590aBBA7Rt2xZAoW++yZMnY8iQIahevToaNmwIU9PCPUdLly7FqlWr4O7uDrlcDgcHB8GqOuWaFMdxMDU1hZ+fn0aefv36YdmyZTh48CC2b9/ODCcYDEaFoSJb7elKuaxJWVtbU0MJq3oQnz9/PgCgY8eO6NixI58ukUiwd+9e4v/d3NwwbNgwyGQyTJ8+nY+ma2Fhga1bt1LbFBERofa7qCqvY8eOuH2brLrx9PSEp6cnAKB9+/Y4ffrjfWwxGAxGacPWpCoAO3fuxLVr15Cfnw9nZ2e1kO8MBoPxX+bfL6IqgZBauHDhp24Cg8FgVEgq8lqTrpSbkEpJScG6detw584dVK9eHQYGBpg4cSJcXV3Lqwka+Pj4ICUlBcePHy+X+kRi8ntNHsV8RZpP9q0nlZHT5QqKBRFFL00LDEfLz2WRDURo4S8AQEK5TfQpoQsK8ii+2yhjR7P8KcgUpozXMyaXz+WSrUhpAQPl6VqC2OmTrdrkCmH+26SUQIw5FF+MpmKyxZycEqyQBjW2ZQHZuk8KsnWfQsv1kptOthQ1SCZb05lSQmbQzg+NS8vI1qDWBuQx1WIITITqS7CAfG5KC3klmEuVi5BSukby8PDA999/DwCIj4/XWBeiIZPJoK9fuk3NyMjAw4cPYWxsjNevX6vtySrLehkMBqO8YDMpHalorpEA4Pz58+jevTusrKwQFhaGKVOmAAAWLVoEiUSCx48fo127dpg5cyZWr16N58+fqxln0NrNYDAYFQVmOKEjFc01EgCEhYVh2rRpsLS0hK+vLy+kACApKQlHjx6Fnp4eNm/ejE6dOmHdunXIyMjAkCFD0LlzZ63tZjAYjIrAv19EfSLDiU/tGik3NxevXr1C+/btIRKJoK+vj2fPnqFJkyZ8GXr/H4r7ypUriIiIwL59+wAA+fn5SEhIgLW1NbXdDAaDURFg6j4dqWiukQIDA5Geno4ePXoAKBReYWFhvJAq6h5p+/btaNhQPfjdjh07qO1mMBiMigBXCeZS5SKkOnXqhM2bN+Pw4cO8U1dV10g1a9aEWCzGyZMnS+Qaae3atUhPT4eJiQnOnz/PCxula6SJEycCKHSN1Lx5c4SFhWHv3r2wty+Movr69WuMGzcOs2fP1qjX2dkZv/zyC5YtWwaRSIRHjx6hRYsWOrdblYREsu82mqe0E1JyNFI3jmIlRvHRZ8CRL9QCim0cza9e4kmyzzg9ETmyMACkgWwdRXEziMQUclk0C0JzPXJb37w0I6br61GsCqMplnH3yS9Cr6Tk8g3/osc/M6lOLitbRo7yakmxCHuXSI5qnKxPPp+mFEu3uJfk60uPcnJo/vAUZ8j9eqdHvt5zc8jpAGBnSg6gmRZDvkveZJGvlyqUKNW5seQ+Tyl4SEz/Gc2I6U/SyOfA/D25XgOKsLAIJEdlBgDjadRDOiOrBEKqXCLzKl0j3bx5Ey4uLvDy8sLChQsxb948eHt74+TJkxgwYABevnyp1TWSTCZD37598f333xNdI3311VewtbVVc4304MEDuLu7o1+/fjhy5AjevHmD+Ph4/v8AULduXZiamuLu3bsa9fr4+EAmk2HAgAHo378/tm3bBgA6t5vBYDA+FZyAT0VFxHGU1+x/EcoQHErru8GDB3/S/Vc0btT2JKZHgyzgXhqQTw1tJpUpJe9LSQF574kpR37rM6TsYbK1Is+k3lJmPwDwnrJXhlY37Q2YNpMy0iPnp80GqDMpyt4zUxPyLOFJBnkm1cSE7GUboM+kYuLJb/efWZPLSksjb9K5zZHPQ6MC8mzTRI886xM6k6paldyvm+lW5PwK+kqJnQ15JqWQk9+no9+Rx452HdH25w3Pf0ZM/9mAPJN6o0fe22ZO0ajQZlJtW5H3ZwGA9YU/qMd0ZXL9ITrn3R3760fXVxZUik1AzDUSg8FgaFJehhNpaWmYPXs24uPjYWtri61bt6J69epqeeLj4zF9+nQoFArIZDKMHDlSbVsSjUohpJhrJAaDwdCkvAwn9uzZAycnJ0yaNAl79uzBnj17eIfhSmrUqIFjx45BIpEgOzsb7u7ucHFxgY2Njdayy2VNisFgMBjljxyczp+P4cKFC/Dw8AAAeHh4IDw8XCOPRCKBRFKo/pdKpVBoUfuqUqozqcTERHz77beIjo6GQqFAt27dsGDBAr5hpcGOHTtw/PhxWFhYQC6XY/bs2bwp+cdgb2+PqKgojfSXL19ixYoVyMjIgFQqhYODA1avXo3IyEj4+PigTp06AABzc3MEBARorSNPTl730Kf4petVkEtMz1SQxzNOTNaT09yGVZGRL5IEir7dWkpufx5HTgcASxF5PYQTkRtV3TiPmB6TQ15vyZaTL+G6EvK6nZgy1rQ1qWpW5HMgSSdbqBka0a37qL4bKRaQ0cnk9RZzA/IakC3Fh16miDxGNQzJfYvOJY+1JSXEr6UR2aejeSq5PTli+vWSTllvMzYm161PUWjlUK7JfMr6bDUDsoUl7dzYyMk+92oaUyJI55DXnan+EEsJIcUfO3YMx44d438PGzaM9+pTHO/fv4e1tTWAwhnT+/fvifkSEhIwadIkxMXFYcGCBcXOooBSFFIcx2H69On46quv4O/vD7lcjmXLlmHLli2lro4bO3YsJkyYgOjoaHh7e+P69esQU0Kwq1ISX3xKrxXKda6nT5/yxxwcHLB7925hjWcwGIxyQiHALq44oTR27FikpKRopM+aNUvtt0gkgojyElqrVi2EhIQgKSkJ06ZNQ+/evWFlRTawUVJqQurGjRswNDTE4MGDAQB6enpYsmQJevTogTp16uDKlSvIyspCUlISBgwYgOnTpwMAgoODcfDgQRQUFKBNmzZYsWIF9PT0YG9vj9GjR+PixYuoUqUKfvjhB43ONGrUCPr6+vjw4QNyc3OxZMkSfPjwARYWFli3bh1q166t4YvP19cXfn5+ePDgAQBg+vTp6N27NwBgy5YtGvUlJyejZs2afJ1NmzYtrSFjMBiMMqU0V6S0aYosLS2RnJwMa2trJCcnw8LCQmtZNjY2aNy4MW7duoU+ffpozVtqa1LPnz9Hy5Yt1dKqVq2KWrVqQS6X4/79+9i+fTtOnTqFs2fP4v79+4iOjsaZM2dw5MgRBAcHQywW85F6c3Jy0KZNG5w6dQoODg7EcBp3796FSCSChYUF/Pz8MGjQIISEhMDd3V0t1LvSF9/ixYvxww8/oGrVqggJCUFISAg6deqktb6xY8dizJgxmDhxIgICApCR8Y8ZtjK8/MCBA+Hv719aQ8lgMBilggKczp+PwcXFBUFBQQCAoKAg4hJMYmIi78QhPT0df//9Nxo0aFBs2eVm3de5c2eYmxfq2F1dXXH79m3o6+vjwYMH8PLyAlDohcLS0hIAYGBggO7duwMAWrVqhatXr/JlBQQE4NSpUzAxMcHWrVshEokQFRWFHTt2AAAGDhyI7777js+v6ovv+vXr2Lx5M39MaSZJq2/w4MFwdnbGn3/+iQsXLuDo0aM4deoUAKbuYzAYFZvysu6bNGkSZs2ahRMnTqB27drYunUrAOD+/fs4evQo1qxZg+joaKxfvx4ikQgcx2H8+PE6aaZKTUh9/vnnOHfunFpaVlYWEhISoKenp6GjVDZ00KBBmDt3rkZ5BgYG/H/EYrGa2yHlmpSuFPXFR0JbfTY2NvDy8oKXlxfc3Nzw7Bl54x+DwWBUJMrLLZK5uTkOHDigkd66dWu0bt0aAPDFF1/wmjIhlJqQcnJywqZNmxAUFAQPDw/I5XKsX78egwYNgpGREa5evYq0tDRUqVIF4eHhWLt2LYyMjODj44OxY8fC0tISaWlpyM7Ohq2treD67e3tERYWBg8PD4SEhMDBwYGYr3Pnzjh06BCWLl0KoHDaWXTTmSqXL1+Gk5MTDAwM8O7dO6SlpcHGxgYvX74U3MZgI7KlUNc88oV0UUK2CDKiXHdVKKY8lnKKVZmIrO2l+frbJyWPU10J3VrLQk62pjKj7Mz/U06uo7YezS8hud6LIPtWk1PCEVvoU/oQTz4HNQ3I7bmURdfF52SRT1AzCSXKL8UqU8xRPIhQ7mZaOS8pY51pSB4jPZCtPo1TahLTq5GbST1nAHBMRD5vYrIhIiwMyddwAaXPqRRPFJFfNyGmn9lJzl9VQU4/TxlTfUrw5ZsvNYOtKllEPaI7lcHBbKmtSSn98509exa9evVC7969YWhoiDlz5gAA7OzsMGPGDAwYMAC9e/dG69at8fnnn2PWrFkYP3483N3dMX78eLx7R3aLUhzLli1DYGAg3N3dERwczAuhokydOhUZGRlwc3PDgAEDEBkZqbXcq1ev8nknTJiA+fPno0aNGiVqI4PBYJQnCgGfikq5+O4LDAzEgwcPsHz58rKuqkIzu/5wYnrXPPK7whPKW2JpzaTElFOfLya/hkZJyBXUpez/AgALinN42kzqThVyn2vLhM2k7kjI5dM2LVpo2etFoiZlevJcn+4NP4fyKGhGmW3SZ1LkdFPK+aeVk0F5Rc2k+OijjZAxxa9iNUp7tM2kHhtQ/AZS8lsoSmcmteJr8jkQOpN6aEieztJUVrmUdgLAole/0A/qyKDP3HXOezJOuCquPKgUbpEYDAaDoQkLH68jnp6e8PQkewBnMBgMRtnwse6OKgJsJsVgMBiVFDaTKiXy8/MxYsQISKVSyOVy9O7dG76+vrh48SK2bdvGu3YfPXo0hg8nr+tow8XFBSYmhb65atSogQ0bNny08UNJ1tm6UdaeCiguRD6XcnghsDhUsQAAIABJREFU0TzWUEqJoURZY8qm+EqrQnEcpqCsz3TOI7dTwtH91dHqpvkTbEJ20QYJpa15lLHrSnYBCFCiFwtdOs6nWEZ2pqydFdZAHosCyhqQjNI3MeXBQ1urollr2lLGlNY32nWqT7uOyM2hxlYCAGfKpUSrm1YLLX8zyvrZ7ztkcJ2h+TikXV8FeuRz2UBKGQtKOUY6OlktKZUgXGDFEFISiQQHDhyAiYkJCgoK4O3tjc6dO2PZsmU4ceIEatasCalUijdv3pS4jgMHDsDCwgKbN2/G7t278c033+j0P7lczm8E/hSQFk9DahYfg4XBYAjDZOnPmon/G1n+DSlFKrLVnq5UCCElEon4mY5MJoNMJoOBgQHkcjnMzAojoEokEjRs2BAAcObMGfzvf/+DWCyGqakpDh06hMDAQERERCA3NxevX79Gz549sWDBAo26HBwccPDgQQBAaGgodu/eDY7j0LVrVz7+ib29PYYNG4Zr165h+fLlkEgkWLt2LXJyciCRSHgfVsnJyZgwYYLW+hgMBuNTURn2SVUIIQUUzlg8PT0RFxcHb29vtGnTBi4uLujevTucnJzQrVs3uLm5QSwW44cffsBPP/0EGxsbNV96jx8/RlBQECQSCfr06YNRo0ahVq1aavVcunQJTZo0QVJSEjZt2oTAwEBUq1YN48ePR3h4OHr27ImcnBzY2dlh0aJFkEql6Nu3L7Zs2QI7OztkZWWhSpUqOtfHYDAYn4rKsCZVYYIe6unpITg4GH/88Qfu3buHZ8+eYc2aNQgICICdnR327duHJUuWACic6SxatAjHjx9Xc1/k5OQEU1NTGBoaolGjRoiPj+ePjRkzBgMHDkRWVhYmT56M+/fvo0OHDrCwsIC+vj7c3d1x8+ZNvi1Kz+gxMTGoUaMG7OzsABQ6zVWG+9BWH4PBYHxq5JxC509FpcLMpJRUq1YNHTt2xJ9//okmTZqgadOmaNq0KQYMGIAePXpg/fr1WLVqFe7evYtLly5h8ODB+O233wBALbiinp6emgBTrknpgqGhoU7rUNrqYzAYjE8NU/eVEqmpqdDX10e1atWQl5eHa9eu4euvv0ZkZCQ6duwIAHjy5Anv0y8uLg5t2rRBmzZtcPnyZSQmJgqu087ODmvWrEFqaiqqV6+OsLAwjBypuUjaoEEDvHv3Dvfu3dNQ9wmFdrnkUDw8GCk4/FprhEa6GeWtJ01MPp1vKH7m6knJE2ljym56WkRVkZYJOc3ikGZxlkUpyoLSpuqUsYihOEuTUozEbChRimkWdvmU9HSKj0EAqE7z/EHJX5Vi+ZWoT/6HhHKBVaW8JCfqka8XaxnZxE5OaSmtxzTPJWItL+007xi086BPuY5MKNcLDQNw+N1GM+CfsYh8zfdwTSKmh14Qpu6vJSJH+C0thAQ9rKhUCCGVnJyMRYsWQS6Xg+M49OnTB46Ojpg9ezaWL1+OKlWqwMjICOvWrQMAbNy4Ea9evQLHcejUqROaNWuGx48fC6rT2toac+fOxZgxY3jDCWX0XVUkEgm2bNkCPz8/5OXloUqVKti/f3+p9JvBYDDKkn+/iCon332MQoJqehPTM/QoPvooHrvNFOQ3XeEzKfIrLW3/FG0mRZsVAQDtfdaYUscHiprVWk7usx7FyPZTzaQoW+EA0GdStNGTUMZV+EyKtn+O3AfaTIrmNZ+mGNemIRCK0JkUbV8dDdrercwynknVV1A39OGLxBOCyiKWYeuic96r8REfXV9ZUCFmUgwGg8EofSqDdR8TUgwGg1FJqchWe7rChFQFQKj6I4eigqCpoOoUkMunqfU+UBbUae54CkQiqvsYmqopj7IIb0jJX0BZns8SkUMsVKGogWjhLGhudISqPqtSVHoAXTVFO2+0/DUoqslEA/KYCg3hQVPr0TCijJGcEkZDASCdouI2l5PLoo0F/Romnx/a9XXYkKx2m0NRx0ncviSXH06O2k0zqCmgnYRSgln3lRPNmzdHkyZNIJfL0bBhQ2zYsEGnkPBFsbe3R1RUFP87ICAA33//Pa5duwZTU9PSbHKpMSThkEZaMGVt61NBE1AMBgmagGKUPpXB5OBfcbVUqVIFwcHBCA0NhYGBAY4ePVoq5YaFhaF169Y4f/488biMsoDMYDAY/wYU4HT+VFT+FTMpVRwcHPD06VMAwP79+/mNvF5eXhg7dqzWdFXi4uKQk5ODFStWYNeuXRg8eDCAQu/m58+fR05ODhQKBX755Rfs3bsXZ86cgVQqhaurK3x9fQEAPj4+SExMRH5+PkaPHo1hwzT3WTAYDManojLMpP5VQkomk+Hy5cvo0qULHjx4gMDAQBw/fhwcx2Ho0KHo0KEDFAoFMb1FixZqZYWFhaFfv35wcHBATEwMUlJSYGVlBQB49OgRTp06BTMzM1y5cgWvXr3CiRMnwHEcpk6dips3b8LR0RFr166FmZkZ8vLy4OXlhV69esHc3PxTDA2DwWBoIK8EftD/FUIqLy8PAwcOBFA4k/Ly8sKRI0fQs2dPGBsbAwBcXV1x69YtcBxHTCcJqZ07d0IsFqNXr144e/Ys73Hiiy++4L2vX716FVevXoWHhwcAICcnB7GxsXB0dMTBgwfx+++/AwASEhLw6tUrJqQYDEaFgXmcKCeUa1KlxdOnTxEbG4vx48cDAKRSKerUqcMLKVWjDI7jMGnSJI1gi5GRkbh27RqOHTsGIyMjjBo1Cvn5+VrrpVmQ0ay7nkgUuFtf00jCgVK+YSldkDRLqlzK5kxtXg6NKa593umT/0VbJK0uI28L/kBpE21M8yk2HjQjq2xKAMgcSkNp9QKAISXgnjHF4oy2yZt2HdHcH9GuCkOBlo606+uVAfkxQrPszNeyEk7zZkS/d2gbzMnl0Iw2xuSRrUQTQN4U3viPG8T0PJElMZ22kfuNHrn80qIyWPf9KwwnSDg4OCA8PBy5ubnIyclBeHg4HBwcqOmqhIWFYcaMGYiIiEBERASuXLmC5ORkohdzZ2dn/Pbbb8jOzgYAJCUl4f3798jMzET16tVhZGSE6Oho3Llzp1z6zWAwGLqi4DidPxWVf8VMikTLli3h6emJIUOGACg0kFCq9GjpSsLCwrBnzx61NFdXV4SFhfHrUkqcnZ0RHR3Nz6SMjY3x3Xff4csvv8TRo0fRt29fNGjQAG3bti2TfjIYDEZJqQwzKea7rxwheTQHtKv7SDhQ3H3RVCI0aJ6iMykbVWnqPm3TcUuKmk6ous9WRvYWnUzZeKxN7UaCpu7To9wdNHUfTc0EAIaUYzR1XwGlTULPM22zOG2MaL4Yaeq+dxRfgjR1nzZP8dYF5GteaJ9pKuhMyjVch3J9SSlX5JeD04jpwUFkdR9tc7m2PYYj3/5CPaYrTWrQFgc0efbu1kfXVxb8a2dSDAaDwdAOc4vEYDAYjApLZVD3VQoh1bRpU7i7u2PTpk0ACvdTOTs7o02bNti9ezf1fykpKVi6dCkSEhIgk8lga2uLH3/8kZr/zZs3mDJlCkJDQzWOjRo1CgsWLEDr1q2p/6e909DUKPVkNOUXuSRLBVllQfOTR/PRR2tPTWowPLrKgqZSMqP4aKOFZPhACUNC9XtIGToxJTutzzTVl4KmitMyFgqB6jVaW2l9E+oDkhaQktZO2rm0LSBfF+m0sCsUlZ42aONNzU9Jr0657qIlZOs+mrr38CmyWq+xXEpMTxORr19zivVracGV00wqLS0Ns2fPRnx8PGxtbbF161ZUr15dI9/bt2/xzTffICEhASKRCHv27EGdOnW0ll0phJSxsTGeP3/OByW8evUqbGxsiv3f9u3b0blzZ4wZMwZAYfRfBoPBqCyUl7ujPXv2wMnJCZMmTcKePXuwZ88ezJ8/XyPfwoULMWXKFHzxxRfIzs6GWFy8gfm/1gS9KF27dsWlS5cAFFrv9e/fnz+WlpYGHx8fuLu7Y+jQobwwSk5ORs2aNfl8zZo1A1C4N2rDhg1wc3ODu7s7Tp8+rVFfXl4eZs+ejb59+2LatGnIy6MHL2MwGIxPAcdxOn8+hgsXLvAODzw8PBAeHq6R58WLF5DJZPjiiy8AACYmJjo5Cq80Qqpfv344ffo08vPz8fTpU7Rp04Y/tmPHDrRo0QIhISGYPXs2Fi5cCAAYMWIEli5dilGjRsHf3x9JSYXRNs+fP48nT54gODgY+/fvx8aNG5GcnKxW35EjR1ClShWcOXMGM2bMwMOHD8uvswwGg6EDQhzMHjt2DJ6envzn2LFjOtfz/v17WFtbAwBq1KiB9+/fa+SJjY1FtWrVMH36dHh4eGDDhg2Qy2mxu/+hUqj7gMJZ0Js3bxAaGoquXbuqHbt9+zZ27NgBAHByckJaWhqysrLQpUsXhIeH488//8Tly5cxaNAghIaG4vbt2+jfvz/09PRgZWUFR0dH3L9/H02bNuXLvHnzJkaNGsXXrXqMwWAwKgJyAWtew4YN0+oke+zYsUhJSdFInzVrltpvkUgEEWH9UiaT4datWwgKCkKtWrUwe/ZsBAYG8ntaaVQaIQUALi4u2LhxI37++WekpZH3MRTFzMwM7u7ucHd3x+TJk3Hz5s0ybiWDwWCUD6Vp3RcQEEA9ZmlpieTkZFhbWyM5ORkWFhYaeWrWrInmzZujbt26AIAePXrg7t27xdZbqYSUl5cXqlWrhqZNmyIyMpJPd3BwwKlTpzBt2jRERkbC3NwcVatWxfXr19G2bVsYGRkhKysLcXFxqFWrFhwcHHDs2DEMGjQI6enpuHXrFhYsWKDmm8/R0RGhoaFwcnLCs2fP+PAh2qBtMKRt6KtLsZpK1ieftmxOQkyn+m6j6KFpE3BavVUpFlMAkENZGKXVTSuJNnY0/3bVBFoP0ozHsijtL8nGZlpU2BRKRF0R5cTVKSCfoTSKNR0NWqRlGlUpb+XZlM3ftOtaQRk7AJBQrSyLaVwRaP4naZuCDykSiOkrZVbE9J+qkK340vVMiOkNpeT2vC/jAJDl5avBxcUFQUFBmDRpEoKCgtCjRw+NPK1bt0ZGRgZSU1NhYWGByMhItGrVqtiyK5WQqlmzJkaPHq2RPn36dCxZsgTu7u4wMjLC+vXrAQAPHz7E6tWroaenB47jMGTIENjZ2aF169aIiorCwIEDIRKJMH/+fNSoUQNv3rzhy/zqq6+wePFi9O3bF40aNULLli3LrZ8MBoOhC+Vl3Tdp0iTMmjULJ06cQO3atbF161YAwP3793H06FGsWbMGenp6WLhwIW9N3bJly2JVfQBzi1SunKC4RaK9cVrJhc2kaHs6hM6kaLOZfMob8KecSeVQ2iR0JkXbq0Q7N6U5k/qgT5nd/UtmUnkicq+zKGMk0VItbSYlcJuU4JnUdnEiMV3oTKothM2kaNcvAIyO/3i3SFbVmuicNyXj2UfXVxZUqpkUg8FgMP5BiOFERYUJKQaDwaiklJe6ryxhQorBYDAqKZVhNafCCyl/f3+EhoZCLBZDLBZj1apVaht1VVm0aBG6deuGPn36UMtbtGgR/vrrL5iamkIsFmP58uWwt7fXyHfkyBEYGRnxu6hLgy9bvCGm33xQm5jeZTK5nPenyZZI75OrEtNjpGQ9eW0x2UtGqpxsJThgM3kv2PPFfxPTASA1twox3dKYXLeZVQ4xvSCPvN6Snk7esV6vA2ULAkX7oWdB9t0m6dGRmP5g4SNievOR9DUGcaN6xPSnfi+J6Z/Z6baNQsn9W9bEdImY3OmWfdKJ6R+iyH2QU3xJ1p5Lvh/vLn1BTK/X8AMxHQBMWpKvPbEF+dp+foi8biuiLOiZmpKjZ58b35CYHrdLMxAqAGytm01M16+aSkxPe0mOwFv34i5iemlRkYMZ6kqFFlJRUVG4dOkSTp48CYlEgtTUVBQUkJ2oCmHBggXo06cPrly5guXLlyMkJETtuEwmw1dfffXR9TAYDManhHlBL2PevXsHc3NzSCSFb1fKDWI7d+7ExYsXkZ+fD3t7e6xatUpjh/ODBw+wfv165OTkwNzcHOvWrePddihxdHREXFwcgEIv5s2aNcPt27fh5uaG7OxsGBsbY8KECXj16hVWrFiB1NRU6OnpYdu2bfjss8+wd+9enDlzBlKpFK6urvD19S2HUWEwGAzdqAwzqQrtu++LL75AQkICevfujZUrV+Kvv/4CAIwcORK//fYbQkNDkZeXh4sXL6r9r6CgAH5+fti+fTsCAwMxePBgbNmyRaP8iIgINGnSRO1/gYGBGD9+vFq+efPmYcSIETh16hSOHj2KGjVq4MqVK3j16hVOnDiB4OBgPHz4kHmrYDAYFQoFp9D5U1Gp0DMpExMTBAYG4tatW4iMjMTs2bMxd+5cmJiYYO/evcjLy0NaWhoaN24MFxcX/n8xMTF49uwZxo0bBwBQKBSoUaMGf3zjxo3w9/eHhYUF1qxZw6f369dPow1ZWVlISkqCq6srAMDQsFC3fPXqVVy9epVfs8rJyUFsbCwcHR1LfyAYDAajBDDDiXJAT08PHTt2RMeOHdGkSRMcO3YMT58+xW+//YZatWphx44dau6KgMIT07hxY6oXX+WaVFF0cRuvWsekSZMwfPhwYR1iMBiMcoIJqTLm5cuXEIvFqF+/PgDg8ePHaNCgAZ4+fQpzc3NkZ2fj3Llz6N27t9r/GjRogNTUVERFRcHe3h4FBQWIjY1F48aNBbehatWqqFmzJsLDw9GzZ09IpVLI5XI4Oztj27ZtcHd3h4mJCZKSkqCvrw9LS3LETgCwvvAHMb0/MZWOySpy+meU/Jq2i6WL3X/QxsRxZOmVZT+pdMrpVjrFQDOeasnoNK6UCtKC/bqyLb/ZnNIpR9PdavlQICVbJ/6bqNBCKicnB35+fsjIyICenh7q1auHVatWwdTUFG5ubrCysiKGa5dIJNi+fTv8/PyQmZkJuVyOMWPGlEhIAYXqweXLl2Pbtm0wMDDAtm3b4OzsjOjoaH4mZWxsjO+++06rkGIwGAyGMJjvPgaDwWBUWCq0dR+DwWAw/tswIcVgMBiMCgsTUgwGg8GosDAhxWAwGIwKCxNSDAaDwaiwMCHFYDAYjAoLE1IMBoPBqLAwIVVOXL9+nf/++vVrtWPnz59X+x0dHc1/l0qlasfu3LlDLP/HH3/kv585c0bt2ObNmzXyZ2VlUdv69u1b6jESMplmTB+h7SkJpVkHqQ+lmb+yIuS6BoDg4GD+++3bt9WO/fLLL6XcOt0Qei8I7XN53AuVGo5RLnh4eBC/F/e7uLwl/Y9q2ujRo4vNP3z4cP77vHnzBJWvax98fX357xs3blQ7Nm7cuI+uQ2gfhOb38/PjvwcEBKgdW7hwoUZ+jhPe59mzZ/Pfv//+e7VjEyZM+Ojyy/ocCM0vdEyFtr9ovbrcC2XdZ4Y6bCZVTnAqjj24Ik4+tP0uLm9J/6Oalp6eTj2mJDc3l//+4oV6xNXiyte1D69eveK/X7t2Te1YaqpmxFOhdQjtg9D8t27d4r8HBQWpHXv69KlGfkB4n1++/CeK759//ql2LCUl5aPLL+tzIDS/0DEV2v6i9epyL5R1nxnqMCFVTqgGZSwaoFHb7+LylvQ/H5Nfl2Mf24eyqONjytflmLaHkZBySrNN5Zm/LK47oWMqtP0laVNZ52eoU6EdzFYmXr9+jSlTpmh8B4A3b96o5U1MTISfnx84juO/A4U3aVJSErH8J0+eoF27duA4Dvn5+WjXrh3/n6LrWgDw/v177N+/HxzH8d+V+UlvnBkZGfj999+hUCiQkZHB6945jkNmZuZHtwconLk8evQICoUCeXl5ePToETiOA8dxyMvL++g6hPZBaH6FQoH09HQoFAr+u/LBKpfLS6XPubm5ePr0KX9c+R0ANb/Q8oXkF3JdA4UzQXd3dwBAXFwc/135/6IIHVOh7QeE3wtC+1ySe4HxD8zBbDmhjCpMo0OHDvz3kydPas07aNCgErcjPT0d1atXx86dO7Xmmz59utrvxYsXa82/bl3JYiYo2wMAo0aN0pr34MGDH1WH0D4Ize/i4gKRSER84xeJRLhw4YJGutA+e3t7a81/+PDhjypfaH4h1zUAxMdrDx1ha2ur9lvomJbkGhJ6Lwjts66o3gsMFUq8msUoE6ZPn65z3lWrVgkuX+hC7a5duwTlDwwMFJS/JAvHV65cKdM6hPZBaP5nz54Jys9xwvt87dq1Mi1faH4h1zXHcdzQoUMF5Rc6pkLbz3HC7wWhfWZGFGTYmlQFg6TyoPH3338LLp8TOHE+e/asoPw///yzoPxC2wMAmzZtKtM6hPZBaP4FCxYIyg8I7/PGjRvLtHyh+YVc1wA0om0Xh9AxFdp+QPi9ILTPJbkX/gswIVXBKOuFVKHlC71xhOYvSX/Luo6y7nNJHkYVrU1lfQ4q2jkryX/Kus//FZiQYmilIt5oFU2Ql8cYVbQ2VbQHakU8B4zSgQmpCoaQt7X/4ht5SagMffi3UxnGlF1HnwYmpCoY8+bN47/TNoAqGT16tNpvuVyOPn36aP1PQECAoPYUV15RlOa1uiK0PcA/FmBpaWlaPyWtQ2gfhOY3MDAQlB/QtHorjpo1a5a4fIVCUex6p9D2qF7XuiB0TU3omAptP0C+F/Ly8tQ2WKtC6rNcLkdSUhLevn3Lf5SU5F74L8BM0MsZ1X0hSkxNTdGqVStMnToV5ubmfLq3tzekUikGDRqEAQMGwNTUtNjyp06dimXLlqF27do6tUe5B0uVqlWrolWrVujZsyefptw7QmPcuHFqv58+fYply5YhKSkJX375JebNm8eb13p5eeHEiRPUsnJzc7Fv3z4kJCTAz88PsbGxiImJQffu3dXyCTVPFtoHoflVSUpKQnx8vNpeHkdHR418JF9vqvTq1UvrcVVu3LiBTp06EY+R6jE1NUWTJk1gaWmpcczDw0PDwwMJ0vWsSkhICDHd3t6eV58VFBRAJpPByMhIq3DUZUxLMp4bNmxAvXr1MHz4cLX0o0eP4s2bN1QBGxERgQ0bNqCgoAARERF4/Pgxtm3bhl27dhHzHzx4EDt37oSVlRXE4n/mB7QxYhTCNvOWM126dIGenh7c3NwAAKdPn0Zubi6srKywePFitQv88OHDiI2NxW+//QZPT0/Y2dnB09MTX3zxBbX8jIwM9O/fH3Z2djAyMuLTaTdOfn4+Xr58yb8lnj9/HnXq1MGTJ08QGRmJpUuXAgCys7MBADExMbh//z5cXFwAABcvXkTr1q01yl25ciWmT5+Otm3b4tdff4W3tzf8/f3x2WefFeucdfHixWjZsiXvTNfGxgYzZ87UEFIRERFayymK0D4o8wvlu+++w5kzZ9CoUSPo6enx6SQhdfHiRQCFG0qjoqJ4IRMZGQl7e3tBQmrRokW4dOkS8diJEydw584ddOzYEUDhXp+WLVvizZs38PHxgYeHh1p+JycnnDt3Dr169dK6FkO7roojKiqK/85xHC5cuEB1ngzoPqYlGc/IyEiideDQoUMxYMAAqpDauXMnTpw4we/Nat68udZ9YD///DPOnj2r9iLK0IEyNnFnFEGbA0o3Nzfif2QyGXf27FnO2dmZ69OnD9e7d2/u3LlzxLyRkZHED40hQ4ZwMpmM/11QUMANHTqUk8lkXN++fTXye3t7c5mZmfzvzMxMztvbWyOfu7u72u/r169zrq6uXFRUVLH7QQYNGsRxHMcNHDiQWp4qRZ2C0tKU6NqHktKrVy8uPz9f0H/GjRvHJSUl8b+TkpK48ePHa+SbNm0a8ePj48O1adOGWv748eO5d+/e8b/fvXvHjR8/nvvw4QPXv39/jfxt27blmjZtyrVs2ZKzt7fn2rZty9nb2wvqk1BUz3dRhI6pruPJcRyx/0r69etHPTZkyBCO49TbTbuHOY7jRo4cyRUUFFCPM8iwmVQ5I5fLce/ePdjZ2QEA7t27x6svVN8QgUJ3KoGBgfjjjz/QuXNn7Nq1Cy1btkRSUhKGDx9OfCsUuts9PT0dOTk5vCoxNzcXaWlp0NPTg0Qi0cifkpKili6RSIiOTQEgMzOTL7dTp07Yvn07fH19NZx4FkUikSAvL49/g4+LiyO2JT8/Hzk5Ofjw4YOau5ysrCyq+yihfVDWc+LECTx//lxt/w7Ny0bdunVRUFBAbDONhIQEWFtb87+trKyIYSIiIyOxfv16GBsbq6VzHKc2OyGVb2Vlxf+2tLREQkICzMzMoK+v+RjQVhaJO3fuYPXq1Xj58iUKCgogl8u1qu9U1XIKhQIPHjyAoaEhtXyhY6rreAKAoaEhYmNjUb9+fbX02NhYrW36/PPPERISArlcjtjYWBw8eBD29vZa+zBq1Ch069ZNrR/a1MYMpu4rd/z8/LB06VJelWRiYoI1a9YgJycHkyZN0sjr5eWFOXPmoEqVKny6Uv1FQqiuf+LEiRg4cCA6duwIjuNw8+ZNTJkyBTk5OXByctLI7+HhAS8vL7i6uvJqGk9PT418X3/9NaKjo9G2bVs+rVmzZggICMAPP/ygdYxmzJiBiRMnIiEhAXPnzkVUVBRRIBw9ehQHDhxAcnIyPD09eSFVtWpVjBw5klq+ah8AIDw8XKurqfnz56Nhw4a4cuUKpk2bhpCQEDRs2JCa38jICB4eHnByclJ7GH3zzTfU/zg5OWHChAno378/gEI1cOfOnTXy2dnZwcTEhLj29Nlnn1HL79ChAyZPnsyrdc+dO4cOHTqovaAAhbHMGjVqhIcPHxLLadmyJTF91apV2LJlC2bOnInffvsNQUFBiI2NpbZHqZYDCl/ObG1tidfF6tWrIRKJBI+pruMJAL6+vvj6668xdepUvn8PHjzAnj17sGTJEmofli1bhl27dkEikWDu3LlwdnaGj48PNX/t2rVRu3ZtFBQUoKCggJqPoQ4znPhEKB2UFmcMkZeXh7dv32p9KNLgVHT92qyrkpOTce/ePQBA69ZQim30AAAgAElEQVStYWNjo7Xchw8f4tatWxCJRHBwcECLFi0Et604Pnz4gLt374LjOLRp0wYWFhbUvAcPHizWZ1tRHjx4wAfdc3R01NoHpRGBu7s7QkJCUFBQgBEjRuD48ePE/DTfi8X5XDx//jwfmsLR0ZEXoqpwHFfiDdDnzp3jX1batWuH3r17a5S1bNkyrF69mjieIpGI6l3D09MTgYGB/BgBuhtfaONj/FjqMp5Knj17hp9++gnPnz8HUDhLmjBhApo2bUrML5fLsWnTJixcuLC4Lmig+oLKKB4mpMoZqVSKc+fOIT4+Xs2AoKgTS0C49RAN0sOipG/MQKEa8ubNm7yQatasGTVvTEwMfvrpJ7x9+1atv6SHHa0turTp2bNnePHihZpX6aLGAKrI5XKkpKSoWYrRLCKV1ogjRozAihUrYGVlhSFDhhAdxiqRSqX8TKJBgwYlMjsvjsTERMTGxqJTp06QSqWQyWQaasDSpKCggNqPESNGYP/+/fjmm29gZWUFa2trBAYG4tSpUxp5w8PDsXfvXt50u1WrVvDx8YGDg4OaiphGeno6EhIStF53JUUZpbdq1arF5h06dCj1RYXEs2fPsGDBAl7dbW5ujg0bNqBx48Yla+x/BKbuK2emTp0KU1NTtGzZslj9ulDrIUB3XX9AQABWr16N9evXaxzT9sZ84MAB/Prrr+jVqxc4jsP8+fMxdOhQ6kxm5syZGD58OIYOHapmdkuC1BZd2rRz505ERkYiOjoaXbt2xeXLl9G+fXuqkBJqCjxs2DCkp6dj5syZmDp1KnJycuDr60tta2RkJBYtWgRbW1twHIeEhARs2LCBaN2n5Pz589i0aRPev3/Ph5YQiURUNe2JEydw6NAhZGZmIjw8HPHx8fj222+pe22Elq+E4zjcuHEDISEhuHTpkkYgQSUbN26EQqHA8uXLERAQgISEBOzYsUMj3+HDh3HixAnMnz+ft6i8f/8+Nm3ahNGjR2PXrl1EwTZq1Cj4+/tDJpPB09MTlpaWaNeuHdVTvdD+BgQEYN++ffyao5mZGXx9fdG/f38kJCSgVq1aGv9p3rw5pkyZgj59+qi9HNAsMpcvX45FixapWRwuW7YMR48eJeZn/D/laaXB0G5JVBSh1kMcx3GLFi3iP0uXLuV++OEHLiUlRSPf6dOnOY7juLi4OJ3bo6w/Ozub/52dna21TUpLvbLEzc2Nk8vlvAXgu3fvuLFjx1Lz9+zZk0tNTS2z9gwaNIiLjo7mf798+bLYcejZsyf34sULnesYMGAAl5+fr/O1IbT8qKgobvXq1VzXrl25tm3bcoGBgVxaWprO/6fRp08f7sOHDxrpqampXOvWrbnDhw8T/6fs5/Hjx7lt27ZxHFd6/d2xYwc3ceJEtXshLi6Omzx5Mrd7926uZ8+exP+p3muqHxokC1VtVquMQthMqpyxt7fH06dPqbpuVYRaDwG6x3Xas2cP+vbtC19f32L1/kVRtUIsapFYlO7du+PQoUNwdXVVmzmamZlR/+Pp6YnBgwfDzc1Np/g6hoaGEIvF0NfXR1ZWFm+5RqNmzZo6bYxWQos3RFLRAoVqMdU1xAYNGhS7UG5paYlGjRrp3CZDQ0O18aQFVRRa/ubNm3H27FnUqlULbm5umDZtGgYPHlzseppyY3VRSCpR0rk3NzdH7dq18dVXXxHLl8vlSE5OxpkzZzBr1qxi+yFkPE+dOoWQkBA1jUPdunWxdetWODk5UT2mC42hVrduXfzvf//DwIED+Xrr1q0rqIz/IkxIlTO3b9/GyZMnYWtrq/aQIamaVK2H5syZgy5dumi1HgIK1ylWr17NqzUcHBywdOlSDTc5ZmZmGD9+PN68eaMWWVQJbd3L09MTQ4YMUbPuGzx4MLU9SgH4008/8Wm0AIBKtmzZgsDAQHh5eaFVq1bw9PSEs7Mz1WCgVatWyMjIwJAhQ+Dp6QljY+NSNQVWVeXk5+fj0qVLWg1ZWrVqhaVLl2LAgAEACs9tq1atqPmV/5k1axZ69uyp1iaa6qhdu3b48ccfkZ+fjxs3buDw4cPo1q3bR5f/66+/on79+vjqq6/g4uICiUSik6HGb7/9xn+XSqU4c+YMcatB1apV8eTJE431pCdPnmh9cfDx8cGECRPQvn172NnZ4fXr1xom46oIGU+xWExUiVepUgXW1tbo0aMHsQ6aqpEmvNauXYsdO3ZgxowZAID27dtj7dq11D4wCmGGE+UMbU2pJL7ESIwbNw5ubm5qb2shISEaLn6kUikePXqEBQsWEF0jadtv9fDhQ9y+fRsikQjt27cvE+s+oHBN7eLFi1i5ciX09PTg6emJ0aNHa52FvXnzBllZWVoX1YXOjIoilUoxYcIEaqRgqVSKQ4cO8daDDg4O8Pb21roGKfSBJ5fLcezYMVy9ehUcx8HZ2RnDhw+nrvvpWr5cLsfVq1cRFhaG69evo2PHjrh+/TouXbpE3E+lDaXFnyq3bt3C/Pnz4enpqWbuHRQUhI0bN8LBwUFQHTSEjOeYMWMwZcoUjS0X169fh7+/P3Ut9Ny5c/z3/Px8hIeHw9raWutWA4ZwmJAqJ7KyslC1alU1x6eqqD54STMbVbRZ9w0cOBDBwcHFpilJTU3Vat5NQoh1X0FBAY4cOcKbAnfo0AHDhg0r1tpNdSNzly5d4O7ujtu3byM4OJjvy8dYA34M6enp8PLywu+//14m5etKQUEBXr16BZFIhHr16gkWIsUhlUpx8eJFhIWF4datW3BycsL3339PzKt6LpQGO0eOHCEaQaSkpODQoUN48eIFAKBRo0YYMWIEatSooZH3xx9/xNdff83vlypKaQiE58+fw8fHB+3bt1cTnH///Tf8/f3x+eef61SOQqGAt7e3hiHEmjVrsHTpUup9XVLXUv8VmLqvnJg7dy52794NT09PDaeoRdVf48ePL3E9ZmZmCA4O5n0DhoaGap15pKenY8uWLRom8aVl3bdy5UrIZDJ+reHUqVNYuXIl1qxZQ22Tp6cnTE1N4eXlhXnz5vEzkDZt2qhZZw0ePBiNGzfmfaEVHdOifZDL5fj111+RmJiIL7/8Us17+Q8//EBVpao6UVUoFEhNTcW0adM08s2cORPbtm2jOl3V5khUVzWtksuXL2P58uWoVasWOI5DUlISVq9eDWdnZ7V8H/OQl0gk6N27N3r37o2srCyEh4dT86paZurr68PW1hZbt24l5rWyssLMmTN12gOoXFcqTl1aFCHj2bhxY4SGhiIkJIQXnA4ODli1apVWjxNFiY2Nxfv37zXSlVqNj7mv/8uwmVQlIz4+HqtXr8adO3cgEolgb2+PZcuWEU1oAWDAgAEYPnw4WrVqpaYqoj0U3N3dcezYMX6dJicnB8OGDaM+gAcMGKDxNk1KU+X169caC8qktICAAJw7dw6mpqbo168fXF1dtW6QXLp0KfLy8tC6dWucOnUKjo6OvFpo0KBBVAMSVRWtvr4+LC0tibOW5ORkWFtbl0ilq6uaVkmfPn3g7++PBg0aACjcj+bj44MzZ86o5YuIiICLi4vOG4w/xvO7EEprDyANoeNZElS9uwBAjRo1MGfOHPTu3ZuY/8CBAxgzZkyxaQx12EzqE1BcyAFt4Q9EIpHWB3xiYqLGjX779m2qkNLX14e3t7euTQcgzLpPT08PcXFxvMue169fF/sfksXhzJkzNdY3xo4di7Fjx+L169cICwvD2LFjUbt2bUyZMgXNmzfXKPfevXu8MB05ciS+/fZbTJ8+HZs3b9YacO7Vq1f8G3arVq2oHjmUvuIOHz6M+fPnqx377rvvNNJUSU1NVTNA8fT0xIEDB6j5jY2NeQEFFFoQqnq9V6L09F6lShX07dtX7VhRgQaUzPP7o0ePsG/fPkRHRwMoHKOJEyeiXr16kMlkRIEudA9gTEwM9u3bp/OMX8h4FhU2Srhi9lYJ9W8YFBSkIZBOnjzJhFQxMCFVzugScoD0NslxHBITE7F7926t5fv5+Wk84ElpSoSaiKta9wGF3gO0WfctWLAAo0ePRt26dcFxHN6+fUu1aIqOjsaLFy+QmZmptik5KytLzbFrUerWrYsePXogLy8Pp06dQkxMDFFIqZqB6+vrY/Xq1di5cydGjx6NnJwcjfwJCQnw8fGBiYkJv1Zx/vx5GBoawt/fH8HBwRgyZIjG/0gbXi9fvqxVSAlV09rZ2WHKlCno27cvRCIRzp49izZt2vBq46IWacotB8Wl6Wo8ouTcuXPYtGkTJk+ejIkTJwIoXM/x9fXFypUrsXXrVqJw0NfXF7QNQLkpfMiQIcVuCgeEjadQYaNkzJgxGn0jpYWGhiI0NFTDkjY7O1unLRb/dZiQKmfCw8Nx9uxZrZZeqmqhR48eISQkBOfOnYOtrS1VlRAVFYWoqCikpqaqqTSysrK07qERaiI+btw4ODo68m+X69at02rd5+TkhPPnz/MucBo2bEjte0xMDC5duoTMzEw1B6QmJiZYvXq1Rn7lDOrChQuoVasW+vXrhylTpqg541WlVatWuHz5Mr788ks+bfr06bCxscHKlSs18n/77bcYNWqUhgPdoKAgDBs2DCKRSE1IHT58GEeOHMHr16/VZsPZ2dnFRu9du3YtVq9ejXXr1vFqWm37cLKzs2FqaoorV64AKByjrKwsnD17FiKRiBdSf/zxBy5fvoykpCQ1K86srCytM1pdPb/v3LkT+/fvR506dfi0Zs2aoVOnTujbty9VPSh0D6DQGb/Q8VTy5MkT3siHZhSUn5+P3Nxcnb3v29vbo0aNGvjw4YPaupSJiYlO+yX/67A1qXJm4sSJ2LZtm9a1k5iYGISFhSE0NBTm5ubo168f9u3bp/bgLspff/2Fv/76C0ePHlWLMGpiYoLu3btr3VMiFF383l2/fp0XUCS0BfOLiooqdtMyUPgwbNq0KXr06IGqVatqqGxID0iFQoE7d+7oFPK9d+/eambGqnz55Zc4efKkWlTbzMxMpKenY/PmzZg7dy6fbmJionVWVBIyMjJQrVq1YvM9efIEjx8/5sOkqLapY8eO1Dd5X19fNGzYEKGhoWqe34saWvTr1w+nT58mlqFt/HJzc7Fr1y5eyCo9iNMMFXbs2AELCwtBm8KFojQKUtUSkIyCDhw4wHvft7GxUfO+P3ToUK0e+BnCYUKqnJkxYwaePHmiNeRAs2bN4ODggDVr1qBevXoACtU32jbAKomPjxe050qoibiufu+UD0Uh+1WOHz+ODh06oH79+uA4DkuWLMH58+dRu3ZtrF+/XsOknLbfSQlNdaWrd+5evXoRhaxCoUCfPn2KDVX+/v17tVkIyYFtSUOX9+zZk4/UXNSij4Q257AkdPX8PmDAAOzatUujb/Hx8ZgyZUqphUZXrq2pQprxl3Q8AeFGQbp63//qq69w5MgRjbWv4ta8GIUwdV854+LiQrzhVNm5cyfCwsIwevRodOnSBf3799e6sK+KVCrFsmXLdF5gFmoirmsIbOVbu4+PD9FSj1a20tosNDQUT58+RXh4OB4/fow1a9bg8OHDavlzc3Mxf/58nDlzRmNtRRu6hkbv1q0bvvnmGyxZskTtwbVu3To1lWFRIiIisH79eiQnJ8PCwgJv375Fo0aNEBYWppG3pKHLz58/jz///BPHjx/HqlWr0L9/fwwaNIgaUyo+Ph6bN2/Gixcv1AQn7cVHaexQrVo1PHv2DFZWVkTzal9fX4wbNw6TJ0/WiMWkbQ1u3Lhx2LZtGz8bTE9Px5w5c9TUzqpERERQy1KlpOOpRIhR0KhRo3Tyvn/kyBEAJV/7+q/DhFQ5M2jQoGLDOPTs2RM9e/ZETk4OLly4gAMHDiA1NRUrVqyAq6ur1jdnoQvM9+/fV7MWdHJy4t35kBDq905XSz2g8KGgHItLly5h4MCBMDc3R+fOnfHdd99p5L98+TLmzZtHNADQxtGjR7F//37o6enB0NCQ+kY7f/58bN68Gd27d+dnp2/fvsWgQYMwe/Zsavlbt27FsWPHMG7cOAQFBeHGjRtUi0ypVEoUlGKxWOuLiVgsRteuXdG1a1dERkZi3rx5CAgIgJ2dHebOnctHflayePFi+Pr6Yu3atfj5558RGBgIhUJBLV/p+X3WrFm853dSoM2ePXuiTp062LdvH3755RcAhXubtm3bpnWT94cPH9TUldWrVycKQaFq45KOJyDc5Zeu3vdpG/iVlLYquLLBhFQ5IySMg7GxMdzd3eHu7o709HScPXsWP/74o1YhJXSBWVcTcaUxhq5+70piqScWi5GcnIzq1avj+vXrapZQeXl5GvmdnZ3h6OiInJwctGvXjhc2pWU6bGBggPnz56NHjx68YP7ss8+Ipt6q6Ovrw9zcHAqFAgqFAp06daJaNJY0dHlGRgZCQkIQFBSEatWqYfHixXB1dcXDhw8xa9YsjZlHfn4+7/bH1tYWM2bMgKenJ1HwKBQKmJiYoHr16nB0dCxWzdysWTNs3LhRa56iiMVivH37llcTxsfHE4XLzZs34eTkRF2PLSqkSjqeQOE13KFDB97lV3FGQefOnUNwcDA8PDywbt06pKSkEGePqhv4ExISeOGckZGBWrVq6TxL/K/ChFQ5s2HDBvz000/8LvuYmBjMnTuXOLNQpXr16hg2bBiGDRumNZ9Qk3JdTcSV+2d0DYEt1FIPKJx1DR48GAqFAi4uLnwwuL/++ovoLXrhwoVYuHAhpk6dCn9/f2pbSFy4cEFtHa579+7EfGKxGH5+foIizFarVg3Z2dlwdHTEvHnzYGFhQQ1GWNLQ5V5eXnBzc8PWrVvV1iDbtm2LoUOHauSXSCRQKBSoV68efvnlF9jY2FD3RInFYuzduxf9+vXTuc9C9zHNmjUL3t7ecHR0BMdxuH37NlatWqWRT6k21tXjeEnHU4menh5EIhFEIlGxmghdve8rhdA333wDV1dXdO3aFUCh5aUu68z/dZjhRDmjGl5bW1pJ0XWBWRWpVKqTiXhJQmbraqmnRCaTaewfycnJAcdxWi0iU1JScP/+fQAoNtz8pk2bcP/+fd5MPCwsDK1atVKzyFNlw4YNaNu2bbFrWKrtVaoRQ0JCkJmZCXd3d+o6XtHQ5Y0bN8b48eOJ5smbN2/GnDlzBIeRv3fvHho1aoTMzExs27YNWVlZmDBhAtq2bUvMv2nTJt6yVHXmSHvZEeq5BCjccHv37l0A9HO2aNEi3uXSyZMniw0ZAggbT1WKuvyiWfcpWblyJebMmYOwsDDs378fxsbGaN68OVWglvW9X1lhQqqcWbx4McRisVoYB7lcLjg2zccSHBwMjuM09OdBQUHQ09Ojer0YNmwYjh07Vmz5H+MzLjc3F/v27UNCQgL8/PwQGxuLmJgY6mznzJkz2LhxIzp06ACO43Dr1i0sWLAAffr0IeZ3d3dHcHAw/zCVy+Xw8PCgPizs7e2Rm5tb7BrWx0Ay/iClaXPfpAu5ubnFqisB4S87JI/nxZGeno5Xr16pqX+Lqr1VLTFL0vecnBzqLLYoQq37VNHF+74y1IjqvX/r1i2qsQijEKbuK2e+/fZbHDp0iA/zoAzjUFroalL+yy+/EEON9+rVCyNHjqQKqWbNmukUMrukjkGBQkHesmVL3LlzBwBgY2ODmTNnUoWUv78/Tpw4we9ZSk1NxdixY6lCCihcD1DOCjIzM7W2R9c1LJp7HSXahJquHiHkcrnaBtKiaPOqsHTpUuTk5ODSpUt48uQJjh49StzEDBQKyKJrONq8fghVM//666/4+eefkZiYiGbNmuHu3bto27athnpQyGxRFaH9VSLEuo/jOJw6dQqvX7/G9OnT8fbtW9y7d0/DaEXJ999/j507d/JbIxwcHKhe5Rn/wIRUOfHy5UtelTZu3Dg1Q4Pbt2+jffv2pVKPriblMpmMqD4zNjbWutYklUphbm6OyMhItfSiQkr5Jq6LeqYocXFx2Lp1K2+ybWRkpNUyi+M4tU21ZmZmWvNPnjwZgwYNQseOHcFxHG7evKnVNFn5MHrz5g2mTZuGhIQEvHv3TuNhpBRmW7duRY0aNdScm757945YtlCPEC9fvoSnpyexf9pmOmvXrsVPP/2EqVOnAih82VC+yJAYPny4xqyFlKZEqOeSn3/+GSdOnMDQoUNx8OBBREdHY8uWLRr5EhMT4efnx7sFKxr7jDYjF9pfQLjLr5UrV0IsFuPGjRuYPn06TExMMGPGDLUAkKqYmZmxWFMlgAmpcqJfv34YOHAgli9friEctPnWE4quJuV5eXlEVUhWVpZWIaWrWvJjYmJJJBLk5eXxb9FxcXFa3Ug5OztjwoQJ6N+/PwDg9OnTWvcxubm5oUOHDvwa1rx584ixjJSoPoymTZsGY2NjfPvtt9SHUUREhNo58Pb2xoABA4iWdDY2NmjVqhUiIiLUNiubmJgQN0J//vnngow4VCnqZJhkGPDu3TskJSUhLy8Pjx49UnP5k5ubSy1bqIWaRCLhZ2pSqRSNGjVCTEyMRj7VPU9CZ+W69FcVVeu+/2vvzIOiOPM3/kwgbAAjngRi3Gg2IgZ0YTXjQQSSNYk6KIegi8Cqi5QiGqMEFNiAyGKVBxpFg6gRgyAIAiIjEGUN2dV4xIP1gpQiCopyoyAC48z7+2N+3TUD3U33cGhMf6qoxOGdnh4Y+tvv93geoGvJr6tXryIrK4tOmZuYmHTZTCSkuUREjRik+ohRo0bBzMwMbm5udCGeoifLgnxbyt3d3fHFF18gMjKS7g67f/8+1q9fD3d3d9bj8/XpoTTKTpw4gdraWjpQHj9+XGvXw8SKFSuwePFiPHz4EIGBgbhy5QpncFyzZg1OnDhBX1zmzZtH3w1rUlpaij/96U+0QR91ztXV1aiurmY1SRR6MTIyMsKxY8cgk8kgkUggl8tZ6yKWlpawtLSEk5OTIEUIoZibm+Py5cuQSCRQKBRITEykU7KanD59GpmZmXj06JHWz9zY2BirV69mPf6zZ8+QkJCAhw8fIioqqss6opmZGZ48eYJp06Zh0aJF6N+/P6MiR8edON+aGt/3C6jTmCkpKSgvL4eFhQXmz5/Py0BSX18fSqWSvpmqr6/nDIRCZxhF/h8i0ie4uLgQQgi5cOEC+etf/0piY2OJUqnU+l5P8PPPPxMHBwfi7e1NvLy8yMcff0zOnj3LuPbQoUPE0dGRSKVSIpVKiaOjI0lOTuY8/sKFC8mRI0eIQqEgCoWCZGRkkIULF7Kud3V15fVYR+rr68mPP/5ITp06Rerq6rpcX1NTQ/7973+TU6dOkdraWsY1//znPwkhhHh7e3f68vHxYT22u7s7ef78Of17qqurI87OzqzrKyoqyNKlS4lUKiUTJ04k/v7+pKKigvP8T506RZydncmHH35IbG1tiY2NDbG1te20LiMjg/M4bNTV1ZHVq1eTyZMnk0mTJpHAwEBSX1/Puj4/P1/Q8VeuXEn27NlDZDIZIYSQlpYWMnv2bF7PPX/+PCkoKCBtbW2say5fvkxmzJhBHBwcCCGEFBcXk4iICNb1Qt7vypUrSWBgIElJSSH+/v7kX//6F6/zzs7OJkuWLCFTp04lW7duJZ999hnJzc1lXc/ncy/SGTFI9RGagejx48dk1apVZN68eaSioqJHgxQhhLS1tZHi4mJSXFzM+YdPCCHl5eWkqamJNDU1aT3GBtOFh+tiNH36dK3jlZeXk+nTp3Oe08WLF8nTp08JIYQcPXqUbNiwgdy/f591/fHjx4mjoyMJDg4mQUFB5OOPPyZ5eXmMa5VKJbl48SLn63eE6WJ0/PhxQcfoimnTppHi4mKiUql4rb9z5w4JCwsjixYtIj4+PvRXT9HW1kaOHTtG4uLiSGxsLP3FBnUB1gzes2bN6rSuoaGB84sNd3d3UllZqXV8KiB2FycnJ/r/FQqFoL/H27dvk6SkJHLw4EFy+/ZtzrU7duwgSUlJpKqqitd7FlEjpvv6CKKR0uvfvz+2bt2KrKwszJ8/n7Nrii+aLeUGBgZ0K2xXLeVCZIsA4b5HISEh8PHx0RoWjoyM5Hwv69atw7Fjx1BSUoIDBw7A3d0da9asoWV3OrJ7927e3X2vvfYaoqKiBNV1Zs+eDSsrK5w7dw6EEHz77besqSPq9dPS0jrVHrhSlmZmZrCwsODdzUaljubOncuZOmIbAaBgK+T7+/vjzTffhJWVFWc9kIJvHVFTfaEjXc3zCakxdWywANQq5dbW1pg2bZrW45qpPT5pvhMnTtCNQkOGDIGXl1eXzwGEN5eIqBGDVB/BZI7n6uqKCRMmYO/evd0+vtCWcl0NBjV9egDgL3/5C+fF197enrefFIW+vj4kEgkKCgowf/58eHh44MiRI6zricDuPr4CsxRBQUHYvHmzVmCiHmNi2bJlGD9+PCZPntxlG7Pm8fz8/CCVSjnlpij4yl9pNhvExsZixYoVvM6nqqpK0PwO3zqirhJAQmpMgLrOdOfOHfpG5cSJE3jnnXdQUlKC8+fPIywsjF5bUlJCW7cQQtDW1qYls9VxdCAuLo4OUgsXLuTd9CTKH+mGGKT6iI53Ww0NDbh48SLMzc0Z5WCEIrSlXBfZIkCt+8bVmcfE9evX8eDBAyiVSpSUlADorBStibGxMeLj45GTk4OkpCSoVCqtHUlHmLr7pk6dyrqer8AsBWUdT6FUKunmCyYodXYhfPPNNzAyMkJbWxtnUwYF37kkzcaD77//nvdIgK2tLX799Vfepnx2dnb44IMP8L///Q+EEISFhXGqfgCgm10kEgkmTJjQaYejCTVGUVVVBXt7e9jZ2SE8PJx1/a+//oqUlBT6JsHT0xNeXl44dOhQpxu24uJiXu+RQvMGiOtmqCNCbXFE1IhBqo9YsmQJAgMDYWFhgerqari5ucHa2hrl5eWYO3cuFi5c2K3jC20pp5TWhcoWVVRUIDo6GkVFRZCExfQAABVsSURBVJBIJLCxsUFoaCijth6g3iFUVFTA0tKSvmBIJBLOILVt2zbI5XJER0dj6NChqKyshK+vL+t6vt19FHyHc+Pj47F79276zhpQX5QMDAwYd8YUjo6O+Omnn2iNNj5UV1dDLpfzXq9L6kjIYOylS5eQlZWFYcOGaQVBLvWF9vZ29O/fH0qlEqWlpSgtLWUUTgbUQae8vJy+sUhJScGZM2cQERHBuH7QoEGCBl8fP36MlpYWWhj42bNnaGxshJ6eHq/0JRdUe75KpUJbW5tWqz4A1i5RobY4ImpEWaQ+QiaT0cOpu3fvxp07d7Bp0yY0NzfD09Oz2/pd3333Hc6ePcvYUi6VSrF48WLG5/FtKaeYO3cu5s+fT9ekjh8/jqSkJKSnpzOunzFjBnJzc3VWDtAFlUoFuVzOajlCeA7nUsTExLDq+jFBySgZGBhAX1+fl4zSpk2bMGXKFF4GhroiRFbowYMHjI+zGWpu3rwZeXl5eP/997VqRWy77unTpyMvL4/+XKhUKshkMuTl5TGuF1JjAtSKFnFxcVoD20uXLoVMJkNsbKwg/cmOcBkdSiQS1rmn2bNnd7JsYXpMRBtxJ9VHaBZkz549SytV9+vXr0dmJnx9fWFkZARvb28tQVY/Pz/O2kVISAicnJywfft2AOq7u5CQENqaoyPPnj3T2gU5Oztz1i5GjRqFmpoamJqa8n4vmvJCCoUCz58/h5GREb1TomhubkZycjKqqqrwySefwM7ODsnJydi/fz9Gjx7NGqSEDud2NBJUKpWIi4tjdf7VxdwuJSUF+/fv7zKwCfVX0vxZtra2au0IuQLnsGHDcPHiRdy7dw9z5sxBfX09q2o6oFZnyM/P571Leffdd1FZWUkHvYcPH9Iu1EwIqTEB6hqwg4MDrl69CgBYtWoV3nrrLQDoVoACQEuaCYXvDKOINmKQ6iPMzc1x8OBBmJmZ4ebNm3TNpLW1lbPeIgRPT094enqiubkZgDoAdkVdXZ2W9Iubmxu+//77Tuso4zZ7e3vs2bMHM2fOhEQiQW5uLmdaq6GhATKZDOPGjdPKvXPVtTQv8uT/zecoHT9NgoKCYGJiAhsbG6SnpyM+Ph6EEOzatQtjxoxhPb7Q4dxz587hxIkTiI6ORmNjI0JCQiCVSlnX//LLL4yPs6W+AP6BTai/kq5usDt37sT169dRVlaGOXPmQKFQICgoCKmpqYzrhw8fDoVCwTtIPX36FDNnzqR3r9euXYO1tTWtVNLx8yGkxkTxhz/8Aaampmhra0N5eTnKy8s5fwdCSU5OxqxZs7TcheVyOWu3H19bHBFtxCDVR0RHR2P79u34+eefsW3bNvqDXVRUBDc3t24fn23nQ8HWJTZw4EBeLeUdW4c1L1YSiYQ1Hca3m4wNiUSCadOmYefOnZ309e7fv0/7SHl4eOCjjz5CYWFhl+Z2QpUCYmJikJubi1mzZsHIyAhbtmzh1FrU3Fm2tbXh6tWrsLKy4pS/uXTpEsaMGQMjIyNkZ2fj5s2bWLBgQScVBqH+Srpy8uRJHD16lG60YPOfolrcDQ0N4eLigsmTJ2sFKrYWd+p98EVojYmvgG13SEtL0wpIJiYmSE9PZw1S1A5YSKeriBik+ozBgwczdvFNmjQJkyZN6vbxqQtIWVkZrl27Rgu8/vjjjxg7dizr8zRbyiUSCWxtbWn/Hk10bZ+VSqVaXk/jxo3rUhZJM5WlUqlw/fp1xsCjmULV09ODmZlZlwEKUNcUAgICUFdXh23btiE/P59RV4/i7t27SExMxOeff47S0lJkZ2fjgw8+YJXn6bgLePjwYZd3zJqzYQkJCfDw8EBwcHCn2TBd/JV04fXXX6fN/wC1bQUTVIu7lZUVo70HG1KpFA8ePMC9e/cwZcoUOqPAtvtfvHgxnJ2dO9WYWlpaaMdhTfgK2HYHlUql5eulVCoZd+S6zjCKqBGDVB/RHcFVPlD1ES8vL2RmZtJ/7MuXL8eSJUtYn8fUUn7gwAHObsPLly/TLeUUbN16ubm52Lx5M+31FBUVxen1BEArlaWnp4dhw4bh22+/7bRO6HwLhdDh3KVLlyIiIgKTJ08GIQQJCQlwd3enG2G6wszMDKWlpZxrNGfDvLy8WGfDqBZ+QH0h7q0gNWPGDISHh+PJkydIS0tDRkYGo+Mv9fqU0SOVjlMqlWhvb2c9flpaGg4fPozHjx+joKAAjx49QkREBGOqGRBeY+IrYNsdPvroI3z55Zf429/+BkCdXWAafdDVFkdEjRik+oiioiKYm5tDJpPhz3/+c4+KympSW1urlUIwMDBAbW2toGNwBSmhLeVC1CAo+KayhM63UAgdzj1y5Agd9CUSCf7xj3+wCqcC2ioPKpUKxcXFnGraAP/ZsN7ukrx37x5qa2vh6+uLM2fOwNjYGGVlZZg6dSpn7XHhwoVISEigZ/VaW1vh6+vLWsNKTk5Geno6HfhGjBiB+vp6znMTUmPiK2DbHYKCgnD48GGkpKQAAKZMmcI4mqCrLY6IGjFI9RFnzpzBmTNncPz4ccjlcjg4OMDJyQmjRo3q0ddxcXGBu7u7lieO0LttrgB6/fp1QS3lQtUgAOFt8ULhO5xLuQv369evk0tuVlYWqyq4psqDnp4eZDJZl35hfGfDdPVX4suGDRvo92VnZwc7OzsA6saFDRs2sO7429ratC7ExsbGnNYeBgYGWjdTXTUPCa0x7dq1C4C6Jjpx4kQ0NTVxDnjrwmuvvUY3KzU2NuLRo0eM3Xq62uKIqBGDVB+hp6cHe3t72Nvbo729HXK5HD4+Pli+fDm8vb177HX8/f0xdepU3p44THAFIKEt5UK9ngDhbfF8ETqcm5ubCz8/PwCdXXL/+9//sgYpV1dXtLe34+7duwCAkSNHdnluQ4cOpZtb6uvrYW5uzrg77Y6/Eh9qa2sZVSZGjx7NOjsFqI0pb9y4QQ+yXr9+HW+88Qbr+g8//BC7d+9Ga2srzpw5g0OHDnHWtITUmJRKJWQyGfLz8wGAsxOzO/j4+CAuLg7Pnz+Hm5sbBg8eDFtbW4SGhmqt09UWR0SNGKT6kPb2dhQWFkIul+PBgwfw8fHhVEbQlTFjxmDo0KF0zaiysrJTqoPN6pyq7XSEqqk9ffqUV0s5lTbqqAZhY2PDOr9EUV9fz6stXihLlizBkiVLeA/ncsnfcO0Gz58/j7Vr12LYsGEghODhw4fYuHEjY2qqqKgIMTExMDExwbJlyxAcHIyGhgaoVCps3LixU0CndsUdd3bUY92lqamJ9Xutra2s3wsNDcXKlSthamoKQghqa2uxdetW1vVfffUVjhw5AgsLCxw+fBgODg6cKh5Cakx6enoYOXIk4+e+J2lqakK/fv2Qnp4OFxcXfPHFF4z1JV1nGEXUiEGqjwgODsatW7dgb2+P5cuXw8LColde5+DBg9i5cyeGDBmi1VbdUdFC6PwMZWLIF8200WeffUbP73SVNgKEK60Lhe9wrmYQ7xjQuXabGzduxHfffYf33nsPgLrjMjAwkFFZfv369Vi9ejWampqwYMEC7N27FzY2NigtLUVgYCDrrrPjzo7tMaFYW1sjLS2tU5NEeno6q9wPoDZvzMvLowPHyJEjWQO5UqlEcHAwYmJiGJsxmBBaY3ry5Al9M6XZhdndBiVNlEolqqurkZeXhy+//JJzrS4zjCJqxCDVRxw7dgyGhoa4e/eu1sQ6H8kcISQmJiI/Px8DBw7skeNRCE2Z6Jo2Apjb4ntyLojvcC7VPajZOQiof2dcnWsKhYIOUID6gs1We1AqlbQU0o4dO2jHZrZuw59++gn/+c9/UFVVpVWPam5u7hH1gtDQUCxfvhw5OTlaqTuFQoGdO3eyPm/evHnIysrSuvlik2HS09NDZWUl2tvbec8JCa0xcY0U9BTLli2Dr68vxo8fj3HjxqGiogIjRoxgXU/tLqurq7Fv3z7cvn0bV65c4dxBiohBqs/QbB3uTczMzOiBx96AKU345ptvwtraGmvXrqWFZnVNGwG6Ka0Lge9wrq7dg9bW1ggLC6PTmjk5Oaz1I83dbscaDtNu7a233oK1tTVOnTqltbMxNjZGSEiITueryZAhQ5Camopz587h1q1bAAAHBwfGWSQAqKmpQVVVFS26Su2empubORsnhg8fDk9PT3zyySdaDQVMQ+e61Jh6qw6lyYwZM7R2rsOHD0dsbCzr+rVr18LNzY3+bI8YMQKrVq0Sg1QXiEHqFWP48OHw8fGBo6MjL18ioSxYsABmZmZaArPl5eWwsrJCaGgovUvUJW2kq0GfUIQO5wolMjISycnJ9M9iwoQJrLUHobs1S0tLWFpawsnJqVctHvgOmZ8+fRqZmZl49OiR1m7X2NiYtbEEUKdc//jHP4IQwqkJCOhWYyoqKkJUVBTu3LkDhUIBpVIJQ0PDHslYUF2fbJ9Xts9pQ0MDZs6ciT179gBQz8b1hG7nq44YpF4x3n77bbz99ttQKBS90t566tQpLdXmefPmwdnZGUFBQVq7H13SRroa9Amlu8O5bFAXUQMDAyxatIjXjYGuu7XTp09j+/btqKysxPPnz3s8bcwXV1dXuLq64ocffsDnn3/O+3ls4rxsCK0xrV+/Htu2bcPKlSuRkZGBo0eP0t2W3YVKxQrtrjQyMkJDQwMd2IqKino16/GqIFp1iAhi3rx5WLBgAT2Mm5+fjwMHDiAtLQ3Ozs7Izs7WWq+ZNnr//fdZ00YdcXFxEWTxLoTm5uZOheuysjJereJcaNZgVqxYwZn66S6ffvopYmNjMXr06D61QelIdnY2nJ2dsX//fsbz6Bioo6OjERYWxqrAwhZ0Lly4wPg4W1rPzc0NmZmZmDVrFt001JufKT7cuHEDUVFRuHXrFkaNGoWGhgZs376dlkkSYUbcSb1i1NfXY+/evbh9+7ZWK3lPCWtu2bIF0dHRiIyMpE0PN2/ejNbWVnz99ded1uuqTdgbF15dh3P5onm/V1FR0a1jdYWZmRksLCxeaIACQNed2LT9OuLs7AxAeLeo0BqToaEh2tvbYWlpiU2bNsHU1BQqlUrQMdjQVeLMysoKSUlJKCsrAyEEI0eOFF15eSAGqVeMr776CjNmzEBhYSEiIyORlZXVpY23EIYPH876RzhhwoQee53eQNfhXL5wtaz3NEFBQfDz84NUKu2V2iNfKKkkpvQdk50I9VkUGnSE1pg2bdoEQggiIiJw4MABPHr0qMd2tkIlzti8v6j0Y0d7FRFtxCD1itHY2AgPDw8kJiZCKpVCKpVqDcbqiq7FYiHoatDHF12Hc/nC1QTR0/Wib775BkZGRmhra3uh0jqLFi3Cvn378M4772g9npGRgbi4uE4ahwEBATqlRPnWmAoKClBVVUXbZXh7e6Ouro7e9XMZK/JFqMQZm/cXhRikuBGD1CsGZV9hamqKwsJCmJqa4vHjx90+rq7FYiHoatDHF12Hc/miaxOELlRXV0Mul/fZ67EREhICX19fxMfH0zNC8fHxkMvlnWxGgO6lRN99910olUro6elhzpw5cHFx6aQcsm/fPi25pPb2dmRmZqKlpQUhISGcwsZ8ESpx1tveX686YpB6xfD390dTUxPWrFmDqKgoPH36tJOWmC5QumqULM+zZ896rGW7r9B1OPdlxN7eHqdPn6YHgV8UDg4OMDAwgJ+fH3bt2oX09HRcu3YNSUlJMDEx6bRe15Qo3xqTQqGAubk5/e/x48djwIABGDBgAOfcllB0lTgrLCzErVu3tOrFQjsdf2+I3X2/A7ryhxLClStXEBYWhpaWFhQWFqKkpASpqalYt25djxxfhB+2trZ49uwZDAwMoK+v/8Ja0CkuXryIgIAA2NraYvv27azmk2PGjIGhoSF9o0ANMHd1/g8ePMCQIUOgUChw4MABNDc3w9PTs1P67tNPP8XJkycZjzFt2jQUFBR0412q0ZQ4k8lkvCXOwsPD0draivPnz8PDwwM//PADxo4dK1rId4EYpH4HODo6orCwsEeO5eHhgR07dsDf359u53VycnopUk8ifQ9VRySEQKFQ0AOqPRU0O9aYPDw86BpTUFBQp/RdYGAgJk6c2GmIPDU1FRcuXOAUveWLpaUlnUXQ3A129Z6pdnjqv0+fPoWfnx8OHTrU7XN6lRHTfb8Devo+RDOdAkCcmn8BXLp0CWPGjIGRkRGys7Nx8+ZNLFiwoFdVv5no7Tqi0BpTaGgoAgICtIbIb9y4gfb2dlr/r7voKnFG7RoNDQ1RVVWFAQMGoKampkfO6VVGDFK/A3qyHdrc3ByXL1+GRCKBQqFAYmIip/W6SO+wbt06HDt2DCUlJUhISICHhweCg4MZmxV+ywitMQ0ePBipqak4e/YsbW7JpT3Ylzg6OuLJkydYvHgx3XEr+kl1jRikXhGE+kPpyrp16xAdHY2qqirY29vDzs4O4eHhPXZ8EX7o6+tDIpGgoKAAXl5e8PDwwJEjR170afU4T5480fq35meNy25+8uTJL0VgAoCrV6/C3NwcAQEBANSDzxYWFnjvvfd6rFb8KiMGqVeE3k67UAwaNAgxMTF98loi7BgbGyM+Ph45OTlISkqCSqXq0oL9t8i4ceMYhYpTU1Mxbty4F3RWwoiIiKBdpX/55Rds2bIFX3/9NYqLixEeHo4dO3a84DN8uREbJ0R4weUlJJFI6LtEkb6hpqYGcrkcY8eOxYQJE1BZWYkLFy4wWs7/lqmrq0NAQABef/11xhrTkCFDXvAZds3s2bNpUebIyEgMGjSIFk9m0rsU0UbcSYnwQtPzh6KlpQUZGRlobGwUg1QfM3ToUFoCqb6+Hubm5q9cgAJe7hoTX6hdrr6+Ps6ePYuoqCj6e0ql8gWe2W8DMUiJ8EJTELS5uRmJiYnIzMzEzJkzBYuFiuhOUVERYmJiYGJigmXLliE4OBgNDQ1QqVTYuHEjq938b52XqcYkFJlMBm9vbwwcOBBvvPEGrXF579490UaeB2K6T4Q3jY2NSEhIQE5ODlxdXfH3v/+dUVVApPdwc3PD6tWr0dTUhPDwcOzduxc2NjYoLS1FYGDgC7WiEGGnqKgINTU1sLOzo7MSZWVlaGlpYTUBFVEj7qREeLFx40acPHkSc+fORU5ODoyNjV/0Kf0uUSqVtBTSjh07YGNjAwDiGMBLDvV70qS7/mW/F8QgJcKLhIQEGBgYIC4uTsuq40XL8fze0BycpoZDKV60t5SISG8gpvtERH5DcGnftbe348aNGy/4DEVEehYxSImIiIiIvLSIomsiIiIiIi8tYpASEREREXlpEYOUiIiIiMhLixikREREREReWv4PZklJ1ss6p9cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "corr = train[quantitative+['SalePrice']].corr()\n",
    "sns.heatmap(corr)\n",
    "plt.figure(2)\n",
    "corr = train[qual_encoded+['SalePrice']].corr()\n",
    "sns.heatmap(corr)\n",
    "plt.figure(3)\n",
    "corr = pd.DataFrame(np.zeros([len(quantitative)+1, len(qual_encoded)+1]), index=quantitative+['SalePrice'], columns=qual_encoded+['SalePrice'])\n",
    "for q1 in quantitative+['SalePrice']:\n",
    "    for q2 in qual_encoded+['SalePrice']:\n",
    "        corr.loc[q1, q2] = train[q1].corr(train[q2])\n",
    "sns.heatmap(corr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.754352675051498\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = quantitative + qual_encoded\n",
    "model = TSNE(n_components=2, random_state=0, perplexity=50)\n",
    "X = train[features].fillna(0.).values\n",
    "tsne = model.fit_transform(X)\n",
    "\n",
    "std = StandardScaler()\n",
    "s = std.fit_transform(X)\n",
    "pca = PCA(n_components=30)\n",
    "pca.fit(s)\n",
    "pc = pca.transform(s)\n",
    "kmeans = KMeans(n_clusters=5)\n",
    "kmeans.fit(pc)\n",
    "\n",
    "fr = pd.DataFrame({'tsne1': tsne[:,0], 'tsne2': tsne[:, 1], 'cluster': kmeans.labels_})\n",
    "sns.lmplot(data=fr, x='tsne1', y='tsne2', hue='cluster', fit_reg=False)\n",
    "print(np.sum(pca.explained_variance_ratio_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "f80e5f49255bd7af3d0348af6e439f1ab27e5dae"
   },
   "source": [
    "### Data processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_uuid": "6809fd6f2b047998499fcc874f3153375e4ce2ff"
   },
   "outputs": [],
   "source": [
    "train.drop(['Id'], axis=1, inplace=True)\n",
    "test.drop(['Id'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_uuid": "c14fb1e14dd78259b6d56fa4345b6cca969e6f80"
   },
   "outputs": [],
   "source": [
    "train = train[train.GrLivArea < 4500]\n",
    "train.reset_index(drop=True, inplace=True)\n",
    "train[\"SalePrice\"] = np.log1p(train[\"SalePrice\"])\n",
    "y = train['SalePrice'].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "19fce170074c59b1bd12652969a61faba9d2e9a4"
   },
   "source": [
    "### Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_uuid": "b8680fdd6ef9abb99796777d3272fa1ffe7776a0"
   },
   "outputs": [],
   "source": [
    "train_features = train.drop(['SalePrice'], axis=1)\n",
    "test_features = test\n",
    "features = pd.concat([train_features, test_features]).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_uuid": "43674957e009d32de93adefbcaba716dd5645825"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2917, 122)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_kg_hide-input": true,
    "_uuid": "cefcd65a3c4cdfb8a23e83b1100b0a04a5c4ba9a"
   },
   "outputs": [],
   "source": [
    "features['MSSubClass'] = features['MSSubClass'].apply(str)\n",
    "features['YrSold'] = features['YrSold'].astype(str)\n",
    "features['MoSold'] = features['MoSold'].astype(str)\n",
    "features['Functional'] = features['Functional'].fillna('Typ') \n",
    "features['Electrical'] = features['Electrical'].fillna(\"SBrkr\") \n",
    "features['KitchenQual'] = features['KitchenQual'].fillna(\"TA\") \n",
    "features[\"PoolQC\"] = features[\"PoolQC\"].fillna(\"None\")\n",
    "features['Exterior1st'] = features['Exterior1st'].fillna(features['Exterior1st'].mode()[0]) \n",
    "features['Exterior2nd'] = features['Exterior2nd'].fillna(features['Exterior2nd'].mode()[0])\n",
    "features['SaleType'] = features['SaleType'].fillna(features['SaleType'].mode()[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>Alley</th>\n",
       "      <th>Alley_E</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>BldgType_E</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtCond_E</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtExposure_E</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinType1_E</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>BsmtFinType2_E</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtQual_E</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>CentralAir_E</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>Condition1_E</th>\n",
       "      <th>Condition2</th>\n",
       "      <th>Condition2_E</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>Electrical_E</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>ExterCond_E</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterQual_E</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior1st_E</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>Exterior2nd_E</th>\n",
       "      <th>...</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>MSZoning_E</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrType_E</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscFeature_E</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Neighborhood_E</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>PavedDrive_E</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>PoolQC_E</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>RoofMatl_E</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>RoofStyle_E</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SaleCondition_E</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleType_E</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>Street</th>\n",
       "      <th>Street_E</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>Utilities_E</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>YrSold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>856</td>\n",
       "      <td>854</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>5.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>3.0</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>706.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>3.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Norm</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Norm</td>\n",
       "      <td>5.0</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>TA</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>3.0</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>12.0</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>4.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>17.0</td>\n",
       "      <td>61</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Gable</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>5.0</td>\n",
       "      <td>WD</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Pave</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8</td>\n",
       "      <td>856.0</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>5.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>4.0</td>\n",
       "      <td>978.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>3.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Feedr</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Norm</td>\n",
       "      <td>5.0</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>TA</td>\n",
       "      <td>4.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>2.0</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>5.0</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Veenker</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Gable</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>5.0</td>\n",
       "      <td>WD</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Pave</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1262.0</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>2.0</td>\n",
       "      <td>298</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>920</td>\n",
       "      <td>866</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>5.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Mn</td>\n",
       "      <td>2.0</td>\n",
       "      <td>486.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>3.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Norm</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Norm</td>\n",
       "      <td>5.0</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>TA</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>3.0</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>12.0</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>4.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>17.0</td>\n",
       "      <td>42</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Gable</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>5.0</td>\n",
       "      <td>WD</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Pave</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>920.0</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>961</td>\n",
       "      <td>756</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>4.0</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>2.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Norm</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Norm</td>\n",
       "      <td>5.0</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>5.0</td>\n",
       "      <td>272</td>\n",
       "      <td>TA</td>\n",
       "      <td>4.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Wd Shng</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Crawfor</td>\n",
       "      <td>18.0</td>\n",
       "      <td>35</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Gable</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>2.0</td>\n",
       "      <td>WD</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Pave</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7</td>\n",
       "      <td>756.0</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1145</td>\n",
       "      <td>1053</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>5.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Av</td>\n",
       "      <td>3.0</td>\n",
       "      <td>655.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>3.0</td>\n",
       "      <td>490.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Norm</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Norm</td>\n",
       "      <td>5.0</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>TA</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>3.0</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>12.0</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>4.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>NoRidge</td>\n",
       "      <td>25.0</td>\n",
       "      <td>84</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Gable</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>5.0</td>\n",
       "      <td>WD</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Pave</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9</td>\n",
       "      <td>1145.0</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>2.0</td>\n",
       "      <td>192</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   1stFlrSF  2ndFlrSF  3SsnPorch   ...   YearBuilt  YearRemodAdd  YrSold\n",
       "0       856       854          0   ...        2003          2003    2008\n",
       "1      1262         0          0   ...        1976          1976    2007\n",
       "2       920       866          0   ...        2001          2002    2008\n",
       "3       961       756          0   ...        1915          1970    2006\n",
       "4      1145      1053          0   ...        2000          2000    2008\n",
       "\n",
       "[5 rows x 122 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_kg_hide-input": true,
    "_uuid": "dee1a00a8d7f02b9750a3d53304428d68e7f712d"
   },
   "outputs": [],
   "source": [
    "for col in ('GarageYrBlt', 'GarageArea', 'GarageCars'):\n",
    "    features[col] = features[col].fillna(0)\n",
    "\n",
    "for col in ['GarageType', 'GarageFinish', 'GarageQual', 'GarageCond']:\n",
    "    features[col] = features[col].fillna('None')\n",
    "\n",
    "for col in ('BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2'):\n",
    "    features[col] = features[col].fillna('None')\n",
    "\n",
    "features['MSZoning'] = features.groupby('MSSubClass')['MSZoning'].transform(lambda x: x.fillna(x.mode()[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_kg_hide-input": true,
    "_uuid": "ddece569289fdbe674d93bfb5e7667ddb96f9ee4"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>Alley</th>\n",
       "      <th>Alley_E</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>BldgType_E</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtCond_E</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtExposure_E</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinType1_E</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>BsmtFinType2_E</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtQual_E</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>CentralAir_E</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>Condition1_E</th>\n",
       "      <th>Condition2</th>\n",
       "      <th>Condition2_E</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>Electrical_E</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>ExterCond_E</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterQual_E</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior1st_E</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>Exterior2nd_E</th>\n",
       "      <th>...</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>MSZoning_E</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrType_E</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscFeature_E</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Neighborhood_E</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>PavedDrive_E</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>PoolQC_E</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>RoofMatl_E</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>RoofStyle_E</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SaleCondition_E</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleType_E</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>Street</th>\n",
       "      <th>Street_E</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>Utilities_E</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>YrSold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>856</td>\n",
       "      <td>854</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>5.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>3.0</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>706.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>3.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Norm</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Norm</td>\n",
       "      <td>5.0</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>TA</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>3.0</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>12.0</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>4.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>17.0</td>\n",
       "      <td>61</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Gable</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>5.0</td>\n",
       "      <td>WD</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Pave</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8</td>\n",
       "      <td>856.0</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>5.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>4.0</td>\n",
       "      <td>978.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>3.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Feedr</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Norm</td>\n",
       "      <td>5.0</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>TA</td>\n",
       "      <td>4.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>2.0</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>5.0</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Veenker</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Gable</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>5.0</td>\n",
       "      <td>WD</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Pave</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1262.0</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>2.0</td>\n",
       "      <td>298</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>920</td>\n",
       "      <td>866</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>5.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Mn</td>\n",
       "      <td>2.0</td>\n",
       "      <td>486.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>3.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Norm</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Norm</td>\n",
       "      <td>5.0</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>TA</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>3.0</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>12.0</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>4.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>17.0</td>\n",
       "      <td>42</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Gable</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>5.0</td>\n",
       "      <td>WD</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Pave</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>920.0</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>961</td>\n",
       "      <td>756</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>4.0</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>2.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Norm</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Norm</td>\n",
       "      <td>5.0</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>5.0</td>\n",
       "      <td>272</td>\n",
       "      <td>TA</td>\n",
       "      <td>4.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Wd Shng</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Crawfor</td>\n",
       "      <td>18.0</td>\n",
       "      <td>35</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Gable</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>2.0</td>\n",
       "      <td>WD</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Pave</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7</td>\n",
       "      <td>756.0</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1145</td>\n",
       "      <td>1053</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>5.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Av</td>\n",
       "      <td>3.0</td>\n",
       "      <td>655.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>3.0</td>\n",
       "      <td>490.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Norm</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Norm</td>\n",
       "      <td>5.0</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>TA</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>3.0</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>12.0</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>4.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>NoRidge</td>\n",
       "      <td>25.0</td>\n",
       "      <td>84</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>Y</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Gable</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>5.0</td>\n",
       "      <td>WD</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Pave</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9</td>\n",
       "      <td>1145.0</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>2.0</td>\n",
       "      <td>192</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   1stFlrSF  2ndFlrSF  3SsnPorch   ...   YearBuilt  YearRemodAdd  YrSold\n",
       "0       856       854          0   ...        2003          2003    2008\n",
       "1      1262         0          0   ...        1976          1976    2007\n",
       "2       920       866          0   ...        2001          2002    2008\n",
       "3       961       756          0   ...        1915          1970    2006\n",
       "4      1145      1053          0   ...        2000          2000    2008\n",
       "\n",
       "[5 rows x 122 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_uuid": "57603f03d91470f0e129b0ed88ec70ba547f3663"
   },
   "outputs": [],
   "source": [
    "objects = []\n",
    "for i in features.columns:\n",
    "    if features[i].dtype == object:\n",
    "        objects.append(i)\n",
    "features.update(features[objects].fillna('None'))\n",
    "\n",
    "features['LotFrontage'] = features.groupby('Neighborhood')['LotFrontage'].transform(lambda x: x.fillna(x.median()))\n",
    "\n",
    "numeric_dtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
    "numerics = []\n",
    "for i in features.columns:\n",
    "    if features[i].dtype in numeric_dtypes:\n",
    "        numerics.append(i)\n",
    "features.update(features[numerics].fillna(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_uuid": "e115eab54fbf0fbc16d5f0ec1202d51d866002b2"
   },
   "outputs": [],
   "source": [
    "numeric_dtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
    "numerics2 = []\n",
    "for i in features.columns:\n",
    "    if features[i].dtype in numeric_dtypes:\n",
    "        numerics2.append(i)\n",
    "skew_features = features[numerics2].apply(lambda x: skew(x)).sort_values(ascending=False)\n",
    "\n",
    "high_skew = skew_features[skew_features > 0.5]\n",
    "skew_index = high_skew.index\n",
    "\n",
    "for i in skew_index:\n",
    "    features[i] = boxcox1p(features[i], boxcox_normmax(features[i] + 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_uuid": "75773fdb4be11c0831d99aad8271d709f4b4ef7f"
   },
   "outputs": [],
   "source": [
    "features = features.drop(['Utilities', 'Street', 'PoolQC',], axis=1)\n",
    "\n",
    "features['YrBltAndRemod']=features['YearBuilt']+features['YearRemodAdd']\n",
    "features['TotalSF']=features['TotalBsmtSF'] + features['1stFlrSF'] + features['2ndFlrSF']\n",
    "\n",
    "features['Total_sqr_footage'] = (features['BsmtFinSF1'] + features['BsmtFinSF2'] +\n",
    "                                 features['1stFlrSF'] + features['2ndFlrSF'])\n",
    "\n",
    "features['Total_Bathrooms'] = (features['FullBath'] + (0.5 * features['HalfBath']) +\n",
    "                               features['BsmtFullBath'] + (0.5 * features['BsmtHalfBath']))\n",
    "\n",
    "features['Total_porch_sf'] = (features['OpenPorchSF'] + features['3SsnPorch'] +\n",
    "                              features['EnclosedPorch'] + features['ScreenPorch'] +\n",
    "                              features['WoodDeckSF'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_uuid": "a187e78c5decf896d62e8076eed6f5bb3ba8c702"
   },
   "outputs": [],
   "source": [
    "features['haspool'] = features['PoolArea'].apply(lambda x: 1 if x > 0 else 0)\n",
    "features['has2ndfloor'] = features['2ndFlrSF'].apply(lambda x: 1 if x > 0 else 0)\n",
    "features['hasgarage'] = features['GarageArea'].apply(lambda x: 1 if x > 0 else 0)\n",
    "features['hasbsmt'] = features['TotalBsmtSF'].apply(lambda x: 1 if x > 0 else 0)\n",
    "features['hasfireplace'] = features['Fireplaces'].apply(lambda x: 1 if x > 0 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_uuid": "aae8e5f66e6da9204c8567f6bac5c0e92fc22ef4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2917, 129)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_uuid": "95eea818f1242ae1ec1b58bc1112fb19b17e4ad0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2917, 376)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_features = pd.get_dummies(features).reset_index(drop=True)\n",
    "final_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "_uuid": "1d1a016b7d8628c3f7be1f1fd2d9964b2b724d40"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1458, 376), (1458,), (1459, 376))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = final_features.iloc[:len(y), :]\n",
    "X_sub = final_features.iloc[len(y):, :]\n",
    "X.shape, y.shape, X_sub.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_uuid": "0a6cb7302e1aa4928b738f65c3d4c05bcf2e267a"
   },
   "outputs": [],
   "source": [
    "outliers = [30, 88, 462, 631, 1322]\n",
    "X = X.drop(X.index[outliers])\n",
    "y = y.drop(y.index[outliers])\n",
    "\n",
    "overfit = []\n",
    "for i in X.columns:\n",
    "    counts = X[i].value_counts()\n",
    "    zeros = counts.iloc[0]\n",
    "    if zeros / len(X) * 100 > 99.94:\n",
    "        overfit.append(i)\n",
    "\n",
    "overfit = list(overfit)\n",
    "X = X.drop(overfit, axis=1)\n",
    "X_sub = X_sub.drop(overfit, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_uuid": "1b4eb5df4ad4e4ef500087fd290aabeb92ad062e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1453, 375), (1453,), (1459, 375))"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape, y.shape, X_sub.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "_uuid": "221673e6ce1fe10acdb5111dc7952f5e1d910c9c"
   },
   "outputs": [],
   "source": [
    "kfolds = KFold(n_splits=10, shuffle=True, random_state=42)\n",
    "\n",
    "def rmsle(y, y_pred):\n",
    "    return np.sqrt(mean_squared_error(y, y_pred))\n",
    "\n",
    "def cv_rmse(model, X=X):\n",
    "    rmse = np.sqrt(-cross_val_score(model, X, y, scoring=\"neg_mean_squared_error\", cv=kfolds))\n",
    "    return (rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "_uuid": "0cec1909ad2525acdb074f9ceefe1888a8e55dab"
   },
   "outputs": [],
   "source": [
    "alphas_alt = [14.5, 14.6, 14.7, 14.8, 14.9, 15, 15.1, 15.2, 15.3, 15.4, 15.5]\n",
    "alphas2 = [5e-05, 0.0001, 0.0002, 0.0003, 0.0004, 0.0005, 0.0006, 0.0007, 0.0008]\n",
    "e_alphas = [0.0001, 0.0002, 0.0003, 0.0004, 0.0005, 0.0006, 0.0007]\n",
    "e_l1ratio = [0.8, 0.85, 0.9, 0.95, 0.99, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "_uuid": "667e3e46ec214c90faa65f7707b269c68e630abb"
   },
   "outputs": [],
   "source": [
    "ridge = make_pipeline(RobustScaler(), RidgeCV(alphas=alphas_alt, cv=kfolds))\n",
    "lasso = make_pipeline(RobustScaler(), LassoCV(max_iter=1e7, alphas=alphas2, random_state=42, cv=kfolds))\n",
    "elasticnet = make_pipeline(RobustScaler(), ElasticNetCV(max_iter=1e7, alphas=e_alphas, cv=kfolds, l1_ratio=e_l1ratio))                                \n",
    "svr = make_pipeline(RobustScaler(), SVR(C= 20, epsilon= 0.008, gamma=0.0003,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "_uuid": "c86cb1ea160210c8a08e7c1a899bcb246d686334"
   },
   "outputs": [],
   "source": [
    "gbr = GradientBoostingRegressor(n_estimators=3000, learning_rate=0.05, max_depth=4, max_features='sqrt', min_samples_leaf=15, min_samples_split=10, loss='huber', random_state =42)                             "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "_uuid": "63e11ef6d6af7e9b0552e87ca2f58660bef1b174"
   },
   "outputs": [],
   "source": [
    "lightgbm = LGBMRegressor(objective='regression', \n",
    "                                       num_leaves=4,\n",
    "                                       learning_rate=0.01, \n",
    "                                       n_estimators=5000,\n",
    "                                       max_bin=200, \n",
    "                                       bagging_fraction=0.75,\n",
    "                                       bagging_freq=5, \n",
    "                                       bagging_seed=7,\n",
    "                                       feature_fraction=0.2,\n",
    "                                       feature_fraction_seed=7,\n",
    "                                       verbose=-1,\n",
    "                                       )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "_uuid": "914205a77a844dc220dcc438d23ec1122ecc83b3"
   },
   "outputs": [],
   "source": [
    "xgboost = XGBRegressor(learning_rate=0.01,n_estimators=3460,\n",
    "                                     max_depth=3, min_child_weight=0,\n",
    "                                     gamma=0, subsample=0.7,\n",
    "                                     colsample_bytree=0.7,\n",
    "                                     objective='reg:linear', nthread=-1,\n",
    "                                     scale_pos_weight=1, seed=27,\n",
    "                                     reg_alpha=0.00006)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "_uuid": "dd688572d658afe67dcf71963e28eff20c16b721"
   },
   "outputs": [],
   "source": [
    "stack_gen = StackingCVRegressor(regressors=(ridge, lasso, elasticnet, gbr, xgboost, lightgbm),\n",
    "                                meta_regressor=xgboost,\n",
    "                                use_features_in_secondary=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "_uuid": "81aae079934ae19a2bb621f3115609770ccb0e6a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LASSO: 0.1005 (0.0147)\n",
      " 2019-05-24 18:21:19.397783\n",
      "elastic net: 0.1003 (0.0147)\n",
      " 2019-05-24 18:24:01.678133\n",
      "SVR: 0.1009 (0.0116)\n",
      " 2019-05-24 18:24:17.510846\n",
      "lightgbm: 0.1058 (0.0149)\n",
      " 2019-05-24 18:24:43.202031\n",
      "gbr: 0.1086 (0.0128)\n",
      " 2019-05-24 18:26:13.596846\n",
      "xgboost: 0.1038 (0.0153)\n",
      " 2019-05-24 18:29:06.726372\n"
     ]
    }
   ],
   "source": [
    "score = cv_rmse(ridge)\n",
    "score = cv_rmse(lasso)\n",
    "print(\"LASSO: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()), datetime.now(), )\n",
    "\n",
    "score = cv_rmse(elasticnet)\n",
    "print(\"elastic net: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()), datetime.now(), )\n",
    "\n",
    "score = cv_rmse(svr)\n",
    "print(\"SVR: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()), datetime.now(), )\n",
    "\n",
    "score = cv_rmse(lightgbm)\n",
    "print(\"lightgbm: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()), datetime.now(), )\n",
    "\n",
    "score = cv_rmse(gbr)\n",
    "print(\"gbr: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()), datetime.now(), )\n",
    "\n",
    "score = cv_rmse(xgboost)\n",
    "print(\"xgboost: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()), datetime.now(), )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "_uuid": "7d39f40cf0a5ce35f211aa1479052c6fdc25b22f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "START Fit\n",
      "stack_gen\n",
      "elasticnet\n",
      "Lasso\n",
      "Ridge\n",
      "Svr\n",
      "GradientBoosting\n",
      "xgboost\n",
      "lightgbm\n"
     ]
    }
   ],
   "source": [
    "print('START Fit')\n",
    "\n",
    "print('stack_gen')\n",
    "stack_gen_model = stack_gen.fit(np.array(X), np.array(y))\n",
    "\n",
    "print('elasticnet')\n",
    "elastic_model_full_data = elasticnet.fit(X, y)\n",
    "\n",
    "print('Lasso')\n",
    "lasso_model_full_data = lasso.fit(X, y)\n",
    "\n",
    "print('Ridge')\n",
    "ridge_model_full_data = ridge.fit(X, y)\n",
    "\n",
    "print('Svr')\n",
    "svr_model_full_data = svr.fit(X, y)\n",
    "\n",
    "print('GradientBoosting')\n",
    "gbr_model_full_data = gbr.fit(X, y)\n",
    "\n",
    "print('xgboost')\n",
    "xgb_model_full_data = xgboost.fit(X, y)\n",
    "\n",
    "print('lightgbm')\n",
    "lgb_model_full_data = lightgbm.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "ec2b00b49c8e397510c01bb0962ff22cb9776b5c"
   },
   "source": [
    "# Blending Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "_uuid": "8d8102c22cf183b3965d3092f3152006ae237a62"
   },
   "outputs": [],
   "source": [
    "def blend_models_predict(X):\n",
    "    return ((0.1 * elastic_model_full_data.predict(X)) + \\\n",
    "            (0.05 * lasso_model_full_data.predict(X)) + \\\n",
    "            (0.1 * ridge_model_full_data.predict(X)) + \\\n",
    "            (0.1 * svr_model_full_data.predict(X)) + \\\n",
    "            (0.1 * gbr_model_full_data.predict(X)) + \\\n",
    "            (0.15 * xgb_model_full_data.predict(X)) + \\\n",
    "            (0.1 * lgb_model_full_data.predict(X)) + \\\n",
    "            (0.3 * stack_gen_model.predict(np.array(X))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "_uuid": "3b972324b0039d12d9422112f9c6a65c84b820f0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSLE score on train data:\n",
      "0.05319122673431779\n"
     ]
    }
   ],
   "source": [
    "print('RMSLE score on train data:')\n",
    "print(rmsle(y, blend_models_predict(X)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "_uuid": "f0fa2dfe20409dc1ee8b31139d6523c62b14d658"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predict submission\n"
     ]
    }
   ],
   "source": [
    "print('Predict submission')\n",
    "submission = pd.read_csv(\"../input/house-prices-advanced-regression-techniques/sample_submission.csv\")\n",
    "submission.iloc[:,1] = np.floor(np.expm1(blend_models_predict(X_sub)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "_uuid": "478804e950bfb156e74f95c3635baea1cd3f1193"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Blend with Top Kernels submissions\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Blend with Top Kernels submissions\\n')\n",
    "sub_1 = pd.read_csv('../input/top-10-0-10943-stacking-mice-and-brutal-force/House_Prices_submit.csv')\n",
    "sub_2 = pd.read_csv('../input/hybrid-svm-benchmark-approach-0-11180-lb-top-2/hybrid_solution.csv')\n",
    "sub_3 = pd.read_csv('../input/lasso-model-for-regression-problem/lasso_sol22_Median.csv')\n",
    "submission.iloc[:,1] = np.floor((0.25 * np.floor(np.expm1(blend_models_predict(X_sub)))) + \n",
    "                                (0.25 * sub_1.iloc[:,1]) + \n",
    "                                (0.25 * sub_2.iloc[:,1]) + \n",
    "                                (0.25 * sub_3.iloc[:,1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "2f91b0dfa25e867d93d4e8fb383f8240d6fcd250"
   },
   "source": [
    "# Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "_kg_hide-input": true,
    "_uuid": "1a3b3c0025fdf118a0bedca994650e8a38170370"
   },
   "outputs": [],
   "source": [
    "q1 = submission['SalePrice'].quantile(0.005)\n",
    "q2 = submission['SalePrice'].quantile(0.995)\n",
    "submission['SalePrice'] = submission['SalePrice'].apply(lambda x: x if x > q1 else x*0.77)\n",
    "submission['SalePrice'] = submission['SalePrice'].apply(lambda x: x if x < q2 else x*1.1)\n",
    "submission.to_csv(\"submission.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "_uuid": "e9a3a97f62fd8e553e8c44b07011f7711e016b32"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>106479.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>141435.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>160652.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>172714.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>166140.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  SalePrice\n",
       "0  1461   106479.0\n",
       "1  1462   141435.0\n",
       "2  1463   160652.0\n",
       "3  1464   172714.0\n",
       "4  1465   166140.0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# New blending"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on: **https://www.kaggle.com/itslek/blend-stack-lr-gb-0-10649-house-prices-v57/data?scriptVersionId=11189608**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "from scipy.stats import skew  # for some statistics\n",
    "from scipy.special import boxcox1p\n",
    "from scipy.stats import boxcox_normmax\n",
    "\n",
    "from sklearn.linear_model import ElasticNetCV, LassoCV, RidgeCV\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from mlxtend.regressor import StackingCVRegressor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set size: (1460, 81)\n",
      "Test set size: (1459, 80)\n",
      "START data processing 2019-05-24 18:35:42.017831\n",
      "(2917, 79)\n",
      "(2917, 86)\n",
      "(2917, 333)\n",
      "X (1458, 333) y (1458,) X_sub (1459, 333)\n",
      "X (1453, 331) y (1453,) X_sub (1459, 331)\n",
      "START ML 2019-05-24 18:35:42.906275\n",
      "TEST score on CV\n",
      "Kernel Ridge score: 0.1024 (0.0143)\n",
      " 2019-05-24 18:36:21.159257\n",
      "Lasso score: 0.1031 (0.0147)\n",
      " 2019-05-24 18:36:52.735451\n",
      "ElasticNet score: 0.1031 (0.0149)\n",
      " 2019-05-24 18:39:00.156670\n",
      "SVR score: 0.1023 (0.0133)\n",
      " 2019-05-24 18:39:13.582227\n",
      "Lightgbm score: 0.1066 (0.0152)\n",
      " 2019-05-24 18:39:37.580485\n",
      "GradientBoosting score: 0.1071 (0.0135)\n",
      " 2019-05-24 18:41:05.064455\n",
      "Xgboost score: 0.1068 (0.0166)\n",
      " 2019-05-24 18:43:38.271982\n",
      "START Fit\n",
      "2019-05-24 18:43:38.272923 StackingCVRegressor\n",
      "2019-05-24 18:48:28.046188 elasticnet\n",
      "2019-05-24 18:48:41.876949 lasso\n",
      "2019-05-24 18:48:44.940862 ridge\n",
      "2019-05-24 18:48:51.325427 svr\n",
      "2019-05-24 18:48:52.835818 GradientBoosting\n",
      "2019-05-24 18:49:01.759756 xgboost\n",
      "2019-05-24 18:49:18.844565 lightgbm\n"
     ]
    }
   ],
   "source": [
    "# Based on https://www.kaggle.com/hemingwei/top-2-from-laurenstc-on-house-price-prediction\n",
    "\n",
    "train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')\n",
    "test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')\n",
    "print(\"Train set size:\", train.shape)\n",
    "print(\"Test set size:\", test.shape)\n",
    "print('START data processing', datetime.now(), )\n",
    "\n",
    "train_ID = train['Id']\n",
    "test_ID = test['Id']\n",
    "# Now drop the  'Id' colum since it's unnecessary for  the prediction process.\n",
    "train.drop(['Id'], axis=1, inplace=True)\n",
    "test.drop(['Id'], axis=1, inplace=True)\n",
    "\n",
    "# Deleting outliers\n",
    "train = train[train.GrLivArea < 4500]\n",
    "train.reset_index(drop=True, inplace=True)\n",
    "\n",
    "# We use the numpy fuction log1p which  applies log(1+x) to all elements of the column\n",
    "train[\"SalePrice\"] = np.log1p(train[\"SalePrice\"])\n",
    "y = train.SalePrice.reset_index(drop=True)\n",
    "train_features = train.drop(['SalePrice'], axis=1)\n",
    "test_features = test\n",
    "\n",
    "features = pd.concat([train_features, test_features]).reset_index(drop=True)\n",
    "print(features.shape)\n",
    "# Some of the non-numeric predictors are stored as numbers; we convert them into strings \n",
    "features['MSSubClass'] = features['MSSubClass'].apply(str)\n",
    "features['YrSold'] = features['YrSold'].astype(str)\n",
    "features['MoSold'] = features['MoSold'].astype(str)\n",
    "\n",
    "features['Functional'] = features['Functional'].fillna('Typ')\n",
    "features['Electrical'] = features['Electrical'].fillna(\"SBrkr\")\n",
    "features['KitchenQual'] = features['KitchenQual'].fillna(\"TA\")\n",
    "features['Exterior1st'] = features['Exterior1st'].fillna(features['Exterior1st'].mode()[0])\n",
    "features['Exterior2nd'] = features['Exterior2nd'].fillna(features['Exterior2nd'].mode()[0])\n",
    "features['SaleType'] = features['SaleType'].fillna(features['SaleType'].mode()[0])\n",
    "\n",
    "features[\"PoolQC\"] = features[\"PoolQC\"].fillna(\"None\")\n",
    "\n",
    "for col in ('GarageYrBlt', 'GarageArea', 'GarageCars'):\n",
    "    features[col] = features[col].fillna(0)\n",
    "for col in ['GarageType', 'GarageFinish', 'GarageQual', 'GarageCond']:\n",
    "    features[col] = features[col].fillna('None')\n",
    "for col in ('BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2'):\n",
    "    features[col] = features[col].fillna('None')\n",
    "\n",
    "features['MSZoning'] = features.groupby('MSSubClass')['MSZoning'].transform(lambda x: x.fillna(x.mode()[0]))\n",
    "\n",
    "objects = []\n",
    "for i in features.columns:\n",
    "    if features[i].dtype == object:\n",
    "        objects.append(i)\n",
    "\n",
    "features.update(features[objects].fillna('None'))\n",
    "\n",
    "features['LotFrontage'] = features.groupby('Neighborhood')['LotFrontage'].transform(lambda x: x.fillna(x.median()))\n",
    "\n",
    "# Filling in the rest of the NA's\n",
    "\n",
    "numeric_dtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
    "numerics = []\n",
    "for i in features.columns:\n",
    "    if features[i].dtype in numeric_dtypes:\n",
    "        numerics.append(i)\n",
    "features.update(features[numerics].fillna(0))\n",
    "\n",
    "numeric_dtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
    "numerics2 = []\n",
    "for i in features.columns:\n",
    "    if features[i].dtype in numeric_dtypes:\n",
    "        numerics2.append(i)\n",
    "\n",
    "skew_features = features[numerics2].apply(lambda x: skew(x)).sort_values(ascending=False)\n",
    "\n",
    "high_skew = skew_features[skew_features > 0.5]\n",
    "skew_index = high_skew.index\n",
    "\n",
    "for i in skew_index:\n",
    "    features[i] = boxcox1p(features[i], boxcox_normmax(features[i] + 1))\n",
    "\n",
    "features = features.drop(['Utilities', 'Street', 'PoolQC',], axis=1)\n",
    "\n",
    "features['YrBltAndRemod']=features['YearBuilt']+features['YearRemodAdd']\n",
    "features['TotalSF']=features['TotalBsmtSF'] + features['1stFlrSF'] + features['2ndFlrSF']\n",
    "\n",
    "features['Total_sqr_footage'] = (features['BsmtFinSF1'] + features['BsmtFinSF2'] +\n",
    "                                 features['1stFlrSF'] + features['2ndFlrSF'])\n",
    "\n",
    "features['Total_Bathrooms'] = (features['FullBath'] + (0.5 * features['HalfBath']) +\n",
    "                               features['BsmtFullBath'] + (0.5 * features['BsmtHalfBath']))\n",
    "\n",
    "features['Total_porch_sf'] = (features['OpenPorchSF'] + features['3SsnPorch'] +\n",
    "                              features['EnclosedPorch'] + features['ScreenPorch'] +\n",
    "                              features['WoodDeckSF'])\n",
    "\n",
    "# simplified features\n",
    "features['haspool'] = features['PoolArea'].apply(lambda x: 1 if x > 0 else 0)\n",
    "features['has2ndfloor'] = features['2ndFlrSF'].apply(lambda x: 1 if x > 0 else 0)\n",
    "features['hasgarage'] = features['GarageArea'].apply(lambda x: 1 if x > 0 else 0)\n",
    "features['hasbsmt'] = features['TotalBsmtSF'].apply(lambda x: 1 if x > 0 else 0)\n",
    "features['hasfireplace'] = features['Fireplaces'].apply(lambda x: 1 if x > 0 else 0)\n",
    "\n",
    "print(features.shape)\n",
    "final_features = pd.get_dummies(features).reset_index(drop=True)\n",
    "print(final_features.shape)\n",
    "\n",
    "X = final_features.iloc[:len(y), :]\n",
    "X_sub = final_features.iloc[len(X):, :]\n",
    "\n",
    "print('X', X.shape, 'y', y.shape, 'X_sub', X_sub.shape)\n",
    "\n",
    "outliers = [30, 88, 462, 631, 1322]\n",
    "X = X.drop(X.index[outliers])\n",
    "y = y.drop(y.index[outliers])\n",
    "\n",
    "overfit = []\n",
    "for i in X.columns:\n",
    "    counts = X[i].value_counts()\n",
    "    zeros = counts.iloc[0]\n",
    "    if zeros / len(X) * 100 > 99.94:\n",
    "        overfit.append(i)\n",
    "\n",
    "overfit = list(overfit)\n",
    "overfit.append('MSZoning_C (all)')\n",
    "\n",
    "X = X.drop(overfit, axis=1).copy()\n",
    "X_sub = X_sub.drop(overfit, axis=1).copy()\n",
    "\n",
    "print('X', X.shape, 'y', y.shape, 'X_sub', X_sub.shape)\n",
    "\n",
    "# ################## ML ########################################\n",
    "print('START ML', datetime.now(), )\n",
    "\n",
    "kfolds = KFold(n_splits=10, shuffle=True, random_state=42)\n",
    "\n",
    "\n",
    "# rmsle\n",
    "def rmsle(y, y_pred):\n",
    "    return np.sqrt(mean_squared_error(y, y_pred))\n",
    "\n",
    "\n",
    "# build our model scoring function\n",
    "def cv_rmse(model, X=X):\n",
    "    rmse = np.sqrt(-cross_val_score(model, X, y,\n",
    "                                    scoring=\"neg_mean_squared_error\",\n",
    "                                    cv=kfolds))\n",
    "    return (rmse)\n",
    "\n",
    "\n",
    "# setup models    \n",
    "alphas_alt = [14.5, 14.6, 14.7, 14.8, 14.9, 15, 15.1, 15.2, 15.3, 15.4, 15.5]\n",
    "alphas2 = [5e-05, 0.0001, 0.0002, 0.0003, 0.0004, 0.0005, 0.0006, 0.0007, 0.0008]\n",
    "e_alphas = [0.0001, 0.0002, 0.0003, 0.0004, 0.0005, 0.0006, 0.0007]\n",
    "e_l1ratio = [0.8, 0.85, 0.9, 0.95, 0.99, 1]\n",
    "\n",
    "ridge = make_pipeline(RobustScaler(),\n",
    "                      RidgeCV(alphas=alphas_alt, cv=kfolds))\n",
    "\n",
    "lasso = make_pipeline(RobustScaler(),\n",
    "                      LassoCV(max_iter=1e7, alphas=alphas2,\n",
    "                              random_state=42, cv=kfolds))\n",
    "\n",
    "elasticnet = make_pipeline(RobustScaler(),\n",
    "                           ElasticNetCV(max_iter=1e7, alphas=e_alphas,\n",
    "                                        cv=kfolds, l1_ratio=e_l1ratio))\n",
    "                                        \n",
    "svr = make_pipeline(RobustScaler(),\n",
    "                      SVR(C= 20, epsilon= 0.008, gamma=0.0003,))\n",
    "\n",
    "\n",
    "gbr = GradientBoostingRegressor(n_estimators=3000, learning_rate=0.05,\n",
    "                                   max_depth=4, max_features='sqrt',\n",
    "                                   min_samples_leaf=15, min_samples_split=10, \n",
    "                                   loss='huber', random_state =42)\n",
    "                                   \n",
    "\n",
    "lightgbm = LGBMRegressor(objective='regression', \n",
    "                                       num_leaves=4,\n",
    "                                       learning_rate=0.01, \n",
    "                                       n_estimators=5000,\n",
    "                                       max_bin=200, \n",
    "                                       bagging_fraction=0.75,\n",
    "                                       bagging_freq=5, \n",
    "                                       bagging_seed=7,\n",
    "                                       feature_fraction=0.2,\n",
    "                                       feature_fraction_seed=7,\n",
    "                                       verbose=-1,\n",
    "                                       #min_data_in_leaf=2,\n",
    "                                       #min_sum_hessian_in_leaf=11\n",
    "                                       )\n",
    "                                       \n",
    "\n",
    "xgboost = XGBRegressor(learning_rate=0.01, n_estimators=3460,\n",
    "                                     max_depth=3, min_child_weight=0,\n",
    "                                     gamma=0, subsample=0.7,\n",
    "                                     colsample_bytree=0.7,\n",
    "                                     objective='reg:linear', nthread=-1,\n",
    "                                     scale_pos_weight=1, seed=27,\n",
    "                                     reg_alpha=0.00006)\n",
    "\n",
    "# stack\n",
    "stack_gen = StackingCVRegressor(regressors=(ridge, lasso, elasticnet,\n",
    "                                            gbr, xgboost, lightgbm),\n",
    "                                meta_regressor=xgboost,\n",
    "                                use_features_in_secondary=True)\n",
    "                                \n",
    "\n",
    "print('TEST score on CV')\n",
    "\n",
    "score = cv_rmse(ridge)\n",
    "print(\"Kernel Ridge score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()), datetime.now(), )\n",
    "\n",
    "score = cv_rmse(lasso)\n",
    "print(\"Lasso score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()), datetime.now(), )\n",
    "\n",
    "score = cv_rmse(elasticnet)\n",
    "print(\"ElasticNet score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()), datetime.now(), )\n",
    "\n",
    "score = cv_rmse(svr)\n",
    "print(\"SVR score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()), datetime.now(), )\n",
    "\n",
    "score = cv_rmse(lightgbm)\n",
    "print(\"Lightgbm score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()), datetime.now(), )\n",
    "\n",
    "score = cv_rmse(gbr)\n",
    "print(\"GradientBoosting score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()), datetime.now(), )\n",
    "\n",
    "score = cv_rmse(xgboost)\n",
    "print(\"Xgboost score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()), datetime.now(), )\n",
    "\n",
    "\n",
    "print('START Fit')\n",
    "print(datetime.now(), 'StackingCVRegressor')\n",
    "stack_gen_model = stack_gen.fit(np.array(X), np.array(y))\n",
    "print(datetime.now(), 'elasticnet')\n",
    "elastic_model_full_data = elasticnet.fit(X, y)\n",
    "print(datetime.now(), 'lasso')\n",
    "lasso_model_full_data = lasso.fit(X, y)\n",
    "print(datetime.now(), 'ridge')\n",
    "ridge_model_full_data = ridge.fit(X, y)\n",
    "print(datetime.now(), 'svr')\n",
    "svr_model_full_data = svr.fit(X, y)\n",
    "print(datetime.now(), 'GradientBoosting')\n",
    "gbr_model_full_data = gbr.fit(X, y)\n",
    "print(datetime.now(), 'xgboost')\n",
    "xgb_model_full_data = xgboost.fit(X, y)\n",
    "print(datetime.now(), 'lightgbm')\n",
    "lgb_model_full_data = lightgbm.fit(X, y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSLE score on train data:\n",
      "0.060326422149531767\n"
     ]
    }
   ],
   "source": [
    "def blend_models_predict(X):\n",
    "    return ((0.1 * elastic_model_full_data.predict(X)) + \\\n",
    "            (0.1 * lasso_model_full_data.predict(X)) + \\\n",
    "            (0.1 * ridge_model_full_data.predict(X)) + \\\n",
    "            (0.1 * svr_model_full_data.predict(X)) + \\\n",
    "            (0.1 * gbr_model_full_data.predict(X)) + \\\n",
    "            (0.15 * xgb_model_full_data.predict(X)) + \\\n",
    "            (0.1 * lgb_model_full_data.predict(X)) + \\\n",
    "            (0.25 * stack_gen_model.predict(np.array(X))))\n",
    "            \n",
    "print('RMSLE score on train data:')\n",
    "print(rmsle(y, blend_models_predict(X)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predict submission 2019-05-24 18:49:23.333502\n",
      "Blend with Top Kernals submissions 2019-05-24 18:49:25.272992\n"
     ]
    }
   ],
   "source": [
    "print('Predict submission', datetime.now(),)\n",
    "submission = pd.read_csv(\"../input/house-prices-advanced-regression-techniques/sample_submission.csv\")\n",
    "submission.iloc[:,1] = np.floor(np.expm1(blend_models_predict(X_sub)))\n",
    "\n",
    "# this kernel gave a score 0.114\n",
    "# let's up it by mixing with the top kernels\n",
    "\n",
    "print('Blend with Top Kernals submissions', datetime.now(),)\n",
    "sub_1 = pd.read_csv('../input/top-10-0-10943-stacking-mice-and-brutal-force/House_Prices_submit.csv')\n",
    "sub_2 = pd.read_csv('../input/hybrid-svm-benchmark-approach-0-11180-lb-top-2/hybrid_solution.csv')\n",
    "sub_3 = pd.read_csv('../input/lasso-model-for-regression-problem/lasso_sol22_Median.csv')\n",
    "\n",
    "submission.iloc[:,1] = np.floor((0.25 * np.floor(np.expm1(blend_models_predict(X_sub)))) + \n",
    "                                (0.25 * sub_1.iloc[:,1]) + \n",
    "                                (0.25 * sub_2.iloc[:,1]) + \n",
    "                                (0.25 * sub_3.iloc[:,1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "_kg_hide-input": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Save submission 2019-05-24 18:49:27.206229\n"
     ]
    }
   ],
   "source": [
    "# Brutal approach to deal with predictions close to outer range \n",
    "q1 = submission['SalePrice'].quantile(0.0045)\n",
    "q2 = submission['SalePrice'].quantile(0.99)\n",
    "\n",
    "submission['SalePrice'] = submission['SalePrice'].apply(lambda x: x if x > q1 else x*0.77)\n",
    "submission['SalePrice'] = submission['SalePrice'].apply(lambda x: x if x < q2 else x*1.1)\n",
    "\n",
    "submission.to_csv(\"new_submission.csv\", index=False)\n",
    "print('Save submission', datetime.now(),)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "_kg_hide-input": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>122030.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>163541.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>184583.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>199052.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>191939.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  SalePrice\n",
       "0  1461   122030.0\n",
       "1  1462   163541.0\n",
       "2  1463   184583.0\n",
       "3  1464   199052.0\n",
       "4  1465   191939.0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [],
   "source": [
    "submission = pd.read_csv('../input/my-best-house-price/House_0.10649.csv')\n",
    "submission.to_csv('best_submission.csv', index=False)"
   ]
  }
 ],
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